Research ArticleLatent Fingerprint Segmentation Based onRidge Density and Orientation Consistency

Manhua Liu 12 Shuxin Liu 3 and Weiwu Yan 4

1Department of Instrument Science and Engineering School of EIEE Shanghai Jiao Tong University Shanghai 200240 China2Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument School of EIEEShanghai Jiao Tong University Shanghai 200240 China3School of Educational Science Minnan Normal University Zhangzhou Fujian 363000 China4Department of Automation School of EIEE Shanghai Jiao Tong University Shanghai 200240 China

Correspondence should be addressed to Shuxin Liu liushuxin01163com and Weiwu Yan yanwwsjtusjtueducn

Received 29 September 2017 Accepted 12 March 2018 Published 17 May 2018

Academic Editor Eryun Liu

Copyright copy 2018 Manhua Liu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Latent fingerprints are captured from the fingerprint impressions left unintentionally at the surfaces of the crime scene Theyare often used as an important evidence to identify criminals in law enforcement agencies Different from the widely usedplain and rolled fingerprints the latent fingerprints are usually of poor quality consisting of complex background with a lot ofnonfingerprint patterns and various noises Latent fingerprint segmentation is an important image processing step to separatefingerprint foreground from background for more accurate and efficient feature extraction and matching Traditional methods areusually based on the local features such as gray scale variance and gradients which are sensitive to noise and cannot work well forlatent imagesThis paper proposes a latent fingerprint segmentationmethod based on combination of ridge density and orientationconsistency which are global and local features of fingerprints respectively First a texture image is obtained by decompositionof latent image with a total variation model Second we propose to detect the ridge segments from the texture image and thencompute the density of ridge segments and ridge orientation consistency to characterize the global and local fingerprint patternsFinally fingerprint segmentation is performed by combining the ridge density and orientation consistency for latent images Theproposedmethod has been evaluated onNIST SD27 latent fingerprint database Experimental results and comparison demonstratethe promising performance of the proposed method

1 Introduction

Latent fingerprints are the finger skin impressions which areunintentionally left on the surface of a crime scene by depositsof oils andor perspiration from the finger [1]This impressionis usually invisible to the naked eye but it can be capturedwith some special techniques Latent fingerprints have beenused as an important evidence to identify criminals in lawenforcement agencies for more than a century Differentfrom the plain and rolled fingerprints which are capturedwith good quality in the controlled environment and widelyused in commercial area latent fingerprints are usually oflow image quality caused by unclear ridge structure unevenimage contrast and various overlapping nonfingerprint pat-terns like lines printed letters handwritings etc as shownin Figure 1 Samples of rolled plain and latent fingerprint

images are shown in Figures 1(a) 1(b) and 1(c) respectivelyDue to the poor quality the performance of automated latentfingerprint identification system (AFIS) (matching latent fin-gerprint to full template fingerprints) is much lower than thatof matching full to full fingerprints [1] Thus it is necessaryto perform the segmentation and enhancement processes forlatent fingerprints before input to AFIS However the latentfingerprints are usually corrupted by noise and the validfingerprint area is small like Figure 1(c)These problems causethe difficulties in segmenting the fingerprint ridge area fromthe latent image

Fingerprint segmentation is an important processing stepto segment the fingerprint foreground with the interleavedridge and valley structure from the complex backgroundwithnonfingerprint patterns for more accurate and efficient fea-ture extraction and identification There are many methods

HindawiSecurity and Communication NetworksVolume 2018 Article ID 4529652 10 pageshttpsdoiorg10115520184529652

2 Security and Communication Networks

(a) (b) (c)

Figure 1 Three types of fingerprints (a) plain (b) rolled and (c) latent

proposed for fingerprint segmentation in the literature [5ndash8]In these methods the fingerprint image is uniformly dividedinto blocks and some local image features such as mean andvariance of gray values and the orientation consistency areextracted to evaluate the fingerprint quality for segmentationThey can work well for plain and rolled fingerprints but theirperformances are not satisfactory for latent fingerprints Themain difficulties may be due to weak ridge structure and thecomplex overlapping irrelevant patterns and various types ofnoise in latent fingerprint imagesThe valid fingerprint regionis small compared to the complex backgroundThus it is stillchallenging for segmentation of latent fingerprints

There are some research efforts made to investigate newmethods for improving the latent fingerprint segmentation[2ndash4 9ndash15] Zhang et al [2] proposed an adaptive directionaltotal variation (ADTV) model for latent fingerprint segmen-tationThis model combines both the anisotropic directionalTV term and the spatially adaptive fidelity weight for moreeffective segmentation of latent fingerprint Short et al [9]proposed a method to apply an adaptable ridge templateto determine a goodness of the fit score in a local regionof fingerprint for segmentation Choi et al [10] proposedto combine both ridge orientation and frequency featuresfor latent fingerprint segmentation In that method theorientation tensor is used to obtain the symmetric patterns offingerprint ridge orientation and the local ridge frequency isestimated by the local Fourier analysisThese two features arecombined for segmentation Karimi-Ashtiani and JayKuo [11]proposed a latent fingerprint segmentation algorithm basedon a quasi-global fingerprintmodel which did not rely on thelocal gradients for estimation of orientations and frequenciesand thus is robust to the gradient deviations Cao et al[12] proposed a method for latent fingerprint segmentationbased on a ridge quality measure which was defined as thestructural similarity between the fingerprint patch and itsdictionary based reconstruction A learning based methodwas proposed for latent fingerprint image segmentation basedon fractal dimension features and weighted extreme learningmachine ensemble [3] The fractal dimension features werecomputed to characterize the texture patterns of image

patches and persistent in geometric quantities Instead ofusing the local image features the linear density which is aglobal feature was computed by detection of line segmentsfor latent fingerprint segmentation [4] A clustering-basedapproach was investigated for latent fingerprints segmen-tation [13] A latent fingerprint segmentation method wasproposed based on orientation and frequency features [14]Recently a deep artificial neural network (DANN)model wasproposed for segmentation of latent fingerprints by using astack of restricted Boltzmann machines (RBMs) to learn thefeatures of fingerprint image patches [15]

This paper proposes a latent fingerprint segmentationmethod based on combination of the ridge density andorientation consistency which are extracted to characterizeboth the global and local texture patterns of fingerprintimage First a total variation (TV) model is used to decom-pose the latent image into texture and cartoon componentsThe cartoon component is removed as the noise while thetexture component consisting of the fingerprint pattern isused for further processing Second to characterize theinterleaved ridge and valley structure of fingerprint wepropose to detect the ridge segments and then compute theridge density and orientation consistency to evaluate thequality of fingerprint image Finally a segmentation mask isgenerated by thresholding and morphological processing foreach latent image The proposed method is tested on NISTSD27 latent fingerprint database and is compared to someexisting methods to demonstrate its performance

The rest of this paper is organized as follows In Section 2we will present the proposed latent fingerprint segmentationalgorithm in detail Section 3 provides the experimentalresults and comparisons Finally Section 4 concludes thispaper

2 The Proposed Method

In this section we present the proposed segmentationalgorithm for latent fingerprints in detail The valid finger-print pattern is often characterized as a number of parallel

Security and Communication Networks 3

Figure 2 The flowchart of the proposed latent fingerprint segmentation algorithm

(a) (b) (c)

Figure 3 (a) A latent fingerprint image and its TV decomposed components (b) cartoon and (c) texture

interleaved ridge and valley flows while the nonfingerprintpattern consists of arch lines printed letters speckle hand-writings stain and other noisesThemain difference betweenthe fingerprint and nonfingerprint patterns is the parallelinterleaved ridge and valley flowsThus we propose to detectthe ridge segments and the region with the dense distributionof parallel ridge segments is segmented as the foregroundof fingerprint image The general flowchart of the proposedlatent fingerprint segmentation algorithm is demonstrated inFigure 2 It consists of four main steps ie decomposition of

latent image into cartoon and texture components detectionof ridge segments computation of ridge density and orienta-tion consistency and latent fingerprint segmentation whichwill be presented in the following subsections accordingly

21 Decomposition of Latent Fingerprint In addition to thefingerprint pattern latent images often contain various typesof nonfingerprint patterns such as arch line characterspeckle handwritings and stain which usually have thesmooth inner surface and sharp edges (see Figure 3(a))

4 Security and Communication Networks

(a) (b) (c)

Figure 4 (a) Binary image with noise removal (b) the reversed binary image with noise removal and (c) the final detected ridge segmentpoints

The total variation (TV) model was proved to be an effi-cient model for regularizing images without smoothing theboundaries of targets [16] To reduce these structured noisesfor latent fingerprint segmentation the TV model is usedto decompose the latent image 119910 into texture and cartooncomponents ie 119910 = 119906 + V Texture component V containsthe oscillatory and noise component while cartoon 119906 consistsof the piecewise smooth component Mathematically thedecomposition is obtained by minimization of total variation[16]

min(119906V)isin1198712(Ω)

TV (119906) + 120582 1003817100381710038171003817119906 minus 119910100381710038171003817100381722 (1)

where 119906 and V denote the image cartoon and texture com-ponents respectively TV(119906) denotes the total variation of119906 and sdot 2 is a two norm The parameter 120582 isin [0 1] isused to balance the tradeoff between the fidelity of 119906 and theminimization of TV Small 120582 will produce weak texture andstrong cartoon image and vice versa In our experiments 120582 isset to 04 to decompose the texture component of latent imagefor fingerprint segmentation

Figures 3(b) and 3(c) show the decomposed cartoonand texture components of the latent image shown in Fig-ure 3(a) respectively The cartoon component consists ofmost nonfingerprint patterns and is removed as noise whilethe texture image contains the weak fingerprint patternAfter decomposition not only the structure noise but alsothe varying illuminations have been significantly reducedfrom the texture image which will facilitate latent fingerprintsegmentation

22 Detection of Ridge Segments After image decompositionby TV model a texture image is obtained and used forfurther processing while the cartoon image with most ofthe structured noises is removed as nonfingerprint patternIt will be easier to extract features from the texture imagefor fingerprint segmentation The texture component oflatent fingerprint is characterized by oscillatory patterns withinterleaved and parallel ridge and valley structure In thiswork we propose to detect the ridge segments from the

texture image to capture the fingerprint patterns The ridgesegment detection procedure can be divided into image bina-rization with noise removal image thinning and orientationcomputation for each ridge point detailed as follows

First the texture image is a gray scale image of [0 255]which is thresholded to a binary image with noise removalFor simplicity the threshold is set to 128 In the binary imagethe black points are the background pixels while the whitepoints are the candidate points of ridge segments Since thelatent fingerprint is often corrupted by various types of noiseit is necessary to remove the noisy points from the binaryimage formore reliable detection of ridge segmentsThenoisypoints usually occur as the isolated points and are removedaccording to the following formula

119868 (119894 119895) =

0 119868 (119894 minus 1 119895) + 119868 (119894 + 1 119895) = 00 119868 (119894 119895 minus 1) + 119868 (119894 119895 + 1) = 00 119868 (119894 minus 1 119895 minus 1) + 119868 (119894 + 1 119895 + 1) = 00 119868 (119894 + 1 119895 minus 1) + 119868 (119894 minus 1 119895 + 1) = 01 elsewise

(2)

Figure 4(a) shows the binary image after removing thenoisy points from the thresholded binary image of textureimage in Figure 3(c) In addition to enhance the detection ofridge segments we also reverse the thresholded binary imagewith the black points changed into white points and viceversaThe noisy points are removed with the same procedureas above These two binary images with noise removal aregenerated for better characterization of the interleaved ridgeand valley pattern Figure 4(b) shows the reversed binaryimage after removing the noisy points

Second the above two binary images are thinnedwith thethinning method in [17] and these two thinned images aresuperimposed into one binary image to form ridge segmentpoints However there are some noisy ridge segment pointswhich are usually characterized to be short closed andcrossed To remove the noisy ridge points for reliable detec-tion of ridge segments we delete the short ridge segments

Security and Communication Networks 5

(a) (b) (c)

Figure 5 (a) The ridge density map (b) orientation consistency map and (c) the final image quality map

with the length smaller than 4 and delete the closed ridgecircles with the number of end points less than 2 For eachridge point we compute the number of ridge points in its3 times 3 neighborhood If it is larger than 3 the point is thecrossed point and this point and all the ridge segment pointsin its 3 times 3 neighborhood are changed to black points Inthis way we go through all ridge points and obtain a set ofconnected and continuous ridge segments which are denotedas 1199031 1199032 119903119899 Figure 4(c) shows the final detected ridgesegment points from Figure 4(b)

Finally we compute the orientation for each ridge pointEach ridge segment is composed of a set of connectedpoints Thus the orientation of each ridge point is computedbased on its neighboring connected ridge points Assumethat the ridge segment 119903119896 consists of 119898 connected pointsdenoted as 1199011198960 1199011198961 119901119896119898 where 1199011198960 119901119896119896 and 119901119896119898 arethe starting middle and ending points of the ridge segment119903119896 respectively The orientation of point 119901119896119896 is computed asfollows

120579119901119896119896 =

arctan((119910119901119896119896+3 minus 119910119901119896119896)(119909119901119896119896+3 minus 119909119901119896119896)) 119896 + 3 le 119898arctan((119910119901119896119898 minus 119910119901119896119898minus3)(119909119901119896119898 minus 119909119901119896119898minus3)) 119896 + 3 gt 119898

(3)

where (119909119901119896119896 119910119901119896119896) is the coordinates of point 119901119896119896 Thus wehave obtained both the location and orientation for each ridgesegment point of the binary image

23 Computation of Ridge Density and Orientation Consis-tency From Figure 4(c) we can see that the valid fingerprintarea should have more parallel ridge segments than the non-fingerprint area Thus after detection of the ridge segmentsand computation of the orientation for each ridge point wepropose to compute a ridge density measure which evaluateshow densely the ridge points are distributed in a local regionwith the specific orientation In addition to the locationsthe orientations of ridge points are also considered becausethe neighboring ridge segments are parallel Thus the wholeorientation range [0 120587) is equally divided into 9 bins as1205731 1205732 1205739 ie 1205731 isin [0 1205879) 1205732 isin [1205879 21205879) 1205739 isin

[81205879 120587) To compute the ridge density we divide the binaryimage with the ridge segment points into the blocks of 16 times16 pixels Let 119878119903 denote the total number of ridge points ineach block and 119878120573119896119903 (119896 = 1 2 9) denote the number ofridge points in each block with the orientation in the bin 120573119896The interleaved ridge and valley flows of fingerprint changesmoothly except for a few singular points Thus the ridgepoints are densely distributed in a local fingerprint regionwith consistent orientationsThe ridge density for block (119894 119895)is computed by sum of the weighted distribution of the ridgepoints with respect to different orientations as follows

Rd (119894 119895) = 9sum119896=1

(119878120573119896119903119878 ) times (119878120573119896119903119878119903 ) (4)

where 119878 is the area of the block The ridge density Rd(119894 119895)is high if the ridge points are densely distributed in a blockand concentrated in one orientation bin Otherwise the ridgedensity is low

In addition the orientations of ridge segments usuallychange slowly in a local neighborhood The orientation ofeach block is defined as the orientation with the maximumnumber of ridge points We compute the orientation con-sistency to evaluate how consistent the orientation of eachblock is with its 8 neighboring orientations in the 3 times 3neighborhood The orientation consistency for block (119894 119895) iscomputed as

Cons (119894 119895)= sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 10038161003816100381610038161003816cos (120579119894+119898119895+119899 minus 120579119894119895)10038161003816100381610038161003816 minus 1sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 (5)

where 119908119894+119898119895+119899 isin 0 1 denotes the availability of orientation120579119894+119898119895+119899 of block (119894 + 119898 119895 + 119899) and Cons(119894 119895) isin [0 1] Ifthe block orientation is consistent with its 8 neighboringorientations Cons(119894 119895) is close to 1 Figures 5(a) and 5(b)show the ridge density map and the orientation consistencymap of Figure 4(c) respectively

24 Latent Fingerprint Segmentation For latent fingerprintsegmentation the ridge density and orientation consistency

6 Security and Communication Networks

(a) (b) (c) (d)

Figure 6 Fingerprint segmentation results by (a) thresholding (b) the biggest mask (c) the convex hull and (d) the final segmentation resultoverlapped with texture image

are combined to generate an image quality measure which iscomputed for each block as

119876 (119894 119895) = Rd (119894 119895) times Cons (119894 119895) (6)

Figure 5(c) shows the image quality map of Figure 4(c)Thresholding is applied to generate a binary segmentationmask for each fingerprint To fill the gaps and eliminatethe islands morphological operations are further applied toobtain the biggest and continuous binary mask The convexhull of the biggestmask is computed as the final segmentationmask Figures 6(a) 6(b) 6(c) and 6(d) show the fingerprintsegmentation results by thresholding the biggest mask itsconvex hull and the final mask overlapped with the textureimage respectively

3 Experimental Results

In this section we conduct experiments on the NIST SD27latent fingerprint database to test the performances of theproposed algorithm NIST SD27 consists of 258 latent finger-prints which are manually assigned into three different levelsof qualities ldquogoodrdquo ldquobadrdquo and ldquouglyrdquo [18] The numbers ofldquogoodrdquo ldquobadrdquo and ldquouglyrdquo fingerprints are 88 85 and 85respectively Our algorithmwas implemented withMATLABsoftware on a computer of 31 GHz Intel Core i5-4440 4GRAM and 64-bit Windows OS For the latent fingerprintimages in NIST SD 27 the average computation time of ouralgorithm is about 5 seconds for one image The manualmarkups of ROI (Region of Interest) by experts providedin [19] are used as the ground truths for NIST SD 27 inour experiments We compare the segmentation results ofthe proposed algorithm with the manual markups and otherpublished methods [2ndash4 10]

First we test the proposed algorithm on the same samplelatent fingerprints of different image qualities from NISTSD27 database as those in [3] The segmentation resultsprovided in [3] are directly used for comparison The seg-mentation results of themethod [2] are downloaded from theauthorrsquos website Figures 7 8 and 9 show the comparisons ofthe segmentation results by themanual markups themethod[2] the method [3] and the proposed method for good badand ugly latent fingerprint images respectively From these

Table 1 Comparison of the segmentation accuracies in differentmethods

Methods MDR FDR AverageMethod [2] 1410 2613 2012Method [10] 1478 4799 3138Method [3] 922 187 1396Method [4] 1332 2421 1876Proposed method 1303 2317 1813

results we can see that the method [2] usually generated alarger segmentation mask which includes the nonfingerprintpatternsThere are some gaps and islands in the segmentationresults by themethod [3] Our proposedmethod can performwell to separate the fingerprint foreground from the complexbackground for latent images of different qualities which iscloser to the manual markups

Second although the segmentation results of latentimages can be evaluated visually we compare the segmenta-tion accuracy of our method with those of some publishedmethods [2ndash4 10] Twomeasures the Missed Detection Rate(MDR) and the False Detection Rate (FDR) are computedfor performance comparison on NIST SD27 The manualsegmentation masks are used as the ground truth MDRis the average percentage of foreground pixels misclassifiedas background while FDR is the average percentage ofbackground pixels misclassified as foreground Table 1 showsthe comparison of the segmentation accuracy in the proposedmethod with those of the existing methods [2ndash4 10] Thesegmentation results are directly used from those reportedin the paper In the comparison the computations of MDRand FDR and the ground truth of segmentation masks by theproposed method are same as those in [2 4 10] The MDRand FDR of our method are 1303 and 2317 respectivelyThese results show the effectiveness of our segmentationmethod compared to the other methods It seems that themethod [3] performed best in comparison But it should benoted that the segmentation ground truth in method [3] wasgenerated by the author of the paper and is different from ourproposed method and the computations of MDR and FDRmeasures were based on image patches instead of pixels

Security and Communication Networks 7

(a) (b) (c)

(d) (e) (f)

Figure 7 (a) Good latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

(a) (b) (c)

(d) (e) (f)

Figure 8 (a) Bad latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

8 Security and Communication Networks

(a) (b) (c)

(d) (e) (f)

Figure 9 (a) Ugly latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

The ultimate goal of segmentation is to improve theperformance of latent fingerprint identification Thus weconduct the latent fingerprint identification experiments totest the effectiveness of the proposed segmentation algorithmfinally The foreground of segmented texture image is theinput of latent image matching The commercial softwareVeriFinger SDK 65 is integrated to perform the featureextraction and matching for latent fingerprint identificationTo make the latent matching experiment more realistic andchallenging the template database includes both the 258 tem-plate fingerprints of NIST SD27 and the 27000 fingerprintsof NIST SD14 Each latent fingerprint is matched against allthe fingerprints of template database and a matching scoreis generated between the latent and a template fingerprintThe matching scores are sorted in descending order andthe identification performance is evaluated by CumulativeMatch Characteristic (CMC) curve A CMC curve plots theidentification accuracy rates with respect to different rank-k(119896 = 1 2 20)

We compare the performances of the proposed algorithmwith those of the linear density based method [4] andthe manual markup as well as those without segmenta-tion which are denoted as ldquolinear density based methodrdquoldquoproposed methodrdquo ldquomanual markuprdquo and ldquowithout seg-mentationrdquo respectively ldquoWithout segmentationrdquo means thetexture image is directly used as input for latent fingerprintidentification Figures 10(a) 10(b) 10(c) and 10(d) show the

comparisons of CMC curves on all latent ldquogoodrdquo ldquobadrdquoand ldquouglyrdquo quality fingerprints of NIST SD27 respectivelyWe can see that the identification of latent fingerprints withsegmentation can achieve much better performance thanthat without segmentation for all latent fingerprints Forbad latent images the proposed algorithm performs betterthan the linear density based method [4] and even betterthan the manual markup The performance is just slightlyworse than the manual markup for latent fingerprints ofgood quality For all fingerprints the proposed algorithmhas better performance than the linear density based method[4] and achieves comparable performance to the manualmarkup These results and comparison demonstrate theeffectiveness of the proposed segmentation algorithm forlatent fingerprints

4 Conclusion

This paper proposes a latent fingerprint segmentation algo-rithm based on combination of ridge density and orientationconsistency First a texture image is generated by imagedecomposition with a TV model and the structured noiseand illumination variation is greatly reduced from the textureimage Second we propose to detect the ridge segments andcompute the ridge density and orientation consistency whichare combined for latent fingerprint segmentation Finally theproposed algorithm is tested on NIST SD27 and compared

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

Security and Communication Networks 3

Figure 2 The flowchart of the proposed latent fingerprint segmentation algorithm

(a) (b) (c)

Figure 3 (a) A latent fingerprint image and its TV decomposed components (b) cartoon and (c) texture

interleaved ridge and valley flows while the nonfingerprintpattern consists of arch lines printed letters speckle hand-writings stain and other noisesThemain difference betweenthe fingerprint and nonfingerprint patterns is the parallelinterleaved ridge and valley flowsThus we propose to detectthe ridge segments and the region with the dense distributionof parallel ridge segments is segmented as the foregroundof fingerprint image The general flowchart of the proposedlatent fingerprint segmentation algorithm is demonstrated inFigure 2 It consists of four main steps ie decomposition of

latent image into cartoon and texture components detectionof ridge segments computation of ridge density and orienta-tion consistency and latent fingerprint segmentation whichwill be presented in the following subsections accordingly

21 Decomposition of Latent Fingerprint In addition to thefingerprint pattern latent images often contain various typesof nonfingerprint patterns such as arch line characterspeckle handwritings and stain which usually have thesmooth inner surface and sharp edges (see Figure 3(a))

4 Security and Communication Networks

(a) (b) (c)

Figure 4 (a) Binary image with noise removal (b) the reversed binary image with noise removal and (c) the final detected ridge segmentpoints

The total variation (TV) model was proved to be an effi-cient model for regularizing images without smoothing theboundaries of targets [16] To reduce these structured noisesfor latent fingerprint segmentation the TV model is usedto decompose the latent image 119910 into texture and cartooncomponents ie 119910 = 119906 + V Texture component V containsthe oscillatory and noise component while cartoon 119906 consistsof the piecewise smooth component Mathematically thedecomposition is obtained by minimization of total variation[16]

min(119906V)isin1198712(Ω)

TV (119906) + 120582 1003817100381710038171003817119906 minus 119910100381710038171003817100381722 (1)

where 119906 and V denote the image cartoon and texture com-ponents respectively TV(119906) denotes the total variation of119906 and sdot 2 is a two norm The parameter 120582 isin [0 1] isused to balance the tradeoff between the fidelity of 119906 and theminimization of TV Small 120582 will produce weak texture andstrong cartoon image and vice versa In our experiments 120582 isset to 04 to decompose the texture component of latent imagefor fingerprint segmentation

Figures 3(b) and 3(c) show the decomposed cartoonand texture components of the latent image shown in Fig-ure 3(a) respectively The cartoon component consists ofmost nonfingerprint patterns and is removed as noise whilethe texture image contains the weak fingerprint patternAfter decomposition not only the structure noise but alsothe varying illuminations have been significantly reducedfrom the texture image which will facilitate latent fingerprintsegmentation

22 Detection of Ridge Segments After image decompositionby TV model a texture image is obtained and used forfurther processing while the cartoon image with most ofthe structured noises is removed as nonfingerprint patternIt will be easier to extract features from the texture imagefor fingerprint segmentation The texture component oflatent fingerprint is characterized by oscillatory patterns withinterleaved and parallel ridge and valley structure In thiswork we propose to detect the ridge segments from the

texture image to capture the fingerprint patterns The ridgesegment detection procedure can be divided into image bina-rization with noise removal image thinning and orientationcomputation for each ridge point detailed as follows

First the texture image is a gray scale image of [0 255]which is thresholded to a binary image with noise removalFor simplicity the threshold is set to 128 In the binary imagethe black points are the background pixels while the whitepoints are the candidate points of ridge segments Since thelatent fingerprint is often corrupted by various types of noiseit is necessary to remove the noisy points from the binaryimage formore reliable detection of ridge segmentsThenoisypoints usually occur as the isolated points and are removedaccording to the following formula

119868 (119894 119895) =

0 119868 (119894 minus 1 119895) + 119868 (119894 + 1 119895) = 00 119868 (119894 119895 minus 1) + 119868 (119894 119895 + 1) = 00 119868 (119894 minus 1 119895 minus 1) + 119868 (119894 + 1 119895 + 1) = 00 119868 (119894 + 1 119895 minus 1) + 119868 (119894 minus 1 119895 + 1) = 01 elsewise

(2)

Figure 4(a) shows the binary image after removing thenoisy points from the thresholded binary image of textureimage in Figure 3(c) In addition to enhance the detection ofridge segments we also reverse the thresholded binary imagewith the black points changed into white points and viceversaThe noisy points are removed with the same procedureas above These two binary images with noise removal aregenerated for better characterization of the interleaved ridgeand valley pattern Figure 4(b) shows the reversed binaryimage after removing the noisy points

Second the above two binary images are thinnedwith thethinning method in [17] and these two thinned images aresuperimposed into one binary image to form ridge segmentpoints However there are some noisy ridge segment pointswhich are usually characterized to be short closed andcrossed To remove the noisy ridge points for reliable detec-tion of ridge segments we delete the short ridge segments

Security and Communication Networks 5

(a) (b) (c)

Figure 5 (a) The ridge density map (b) orientation consistency map and (c) the final image quality map

with the length smaller than 4 and delete the closed ridgecircles with the number of end points less than 2 For eachridge point we compute the number of ridge points in its3 times 3 neighborhood If it is larger than 3 the point is thecrossed point and this point and all the ridge segment pointsin its 3 times 3 neighborhood are changed to black points Inthis way we go through all ridge points and obtain a set ofconnected and continuous ridge segments which are denotedas 1199031 1199032 119903119899 Figure 4(c) shows the final detected ridgesegment points from Figure 4(b)

Finally we compute the orientation for each ridge pointEach ridge segment is composed of a set of connectedpoints Thus the orientation of each ridge point is computedbased on its neighboring connected ridge points Assumethat the ridge segment 119903119896 consists of 119898 connected pointsdenoted as 1199011198960 1199011198961 119901119896119898 where 1199011198960 119901119896119896 and 119901119896119898 arethe starting middle and ending points of the ridge segment119903119896 respectively The orientation of point 119901119896119896 is computed asfollows

120579119901119896119896 =

arctan((119910119901119896119896+3 minus 119910119901119896119896)(119909119901119896119896+3 minus 119909119901119896119896)) 119896 + 3 le 119898arctan((119910119901119896119898 minus 119910119901119896119898minus3)(119909119901119896119898 minus 119909119901119896119898minus3)) 119896 + 3 gt 119898

(3)

where (119909119901119896119896 119910119901119896119896) is the coordinates of point 119901119896119896 Thus wehave obtained both the location and orientation for each ridgesegment point of the binary image

23 Computation of Ridge Density and Orientation Consis-tency From Figure 4(c) we can see that the valid fingerprintarea should have more parallel ridge segments than the non-fingerprint area Thus after detection of the ridge segmentsand computation of the orientation for each ridge point wepropose to compute a ridge density measure which evaluateshow densely the ridge points are distributed in a local regionwith the specific orientation In addition to the locationsthe orientations of ridge points are also considered becausethe neighboring ridge segments are parallel Thus the wholeorientation range [0 120587) is equally divided into 9 bins as1205731 1205732 1205739 ie 1205731 isin [0 1205879) 1205732 isin [1205879 21205879) 1205739 isin

[81205879 120587) To compute the ridge density we divide the binaryimage with the ridge segment points into the blocks of 16 times16 pixels Let 119878119903 denote the total number of ridge points ineach block and 119878120573119896119903 (119896 = 1 2 9) denote the number ofridge points in each block with the orientation in the bin 120573119896The interleaved ridge and valley flows of fingerprint changesmoothly except for a few singular points Thus the ridgepoints are densely distributed in a local fingerprint regionwith consistent orientationsThe ridge density for block (119894 119895)is computed by sum of the weighted distribution of the ridgepoints with respect to different orientations as follows

Rd (119894 119895) = 9sum119896=1

(119878120573119896119903119878 ) times (119878120573119896119903119878119903 ) (4)

where 119878 is the area of the block The ridge density Rd(119894 119895)is high if the ridge points are densely distributed in a blockand concentrated in one orientation bin Otherwise the ridgedensity is low

In addition the orientations of ridge segments usuallychange slowly in a local neighborhood The orientation ofeach block is defined as the orientation with the maximumnumber of ridge points We compute the orientation con-sistency to evaluate how consistent the orientation of eachblock is with its 8 neighboring orientations in the 3 times 3neighborhood The orientation consistency for block (119894 119895) iscomputed as

Cons (119894 119895)= sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 10038161003816100381610038161003816cos (120579119894+119898119895+119899 minus 120579119894119895)10038161003816100381610038161003816 minus 1sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 (5)

where 119908119894+119898119895+119899 isin 0 1 denotes the availability of orientation120579119894+119898119895+119899 of block (119894 + 119898 119895 + 119899) and Cons(119894 119895) isin [0 1] Ifthe block orientation is consistent with its 8 neighboringorientations Cons(119894 119895) is close to 1 Figures 5(a) and 5(b)show the ridge density map and the orientation consistencymap of Figure 4(c) respectively

24 Latent Fingerprint Segmentation For latent fingerprintsegmentation the ridge density and orientation consistency

6 Security and Communication Networks

(a) (b) (c) (d)

Figure 6 Fingerprint segmentation results by (a) thresholding (b) the biggest mask (c) the convex hull and (d) the final segmentation resultoverlapped with texture image

are combined to generate an image quality measure which iscomputed for each block as

119876 (119894 119895) = Rd (119894 119895) times Cons (119894 119895) (6)

Figure 5(c) shows the image quality map of Figure 4(c)Thresholding is applied to generate a binary segmentationmask for each fingerprint To fill the gaps and eliminatethe islands morphological operations are further applied toobtain the biggest and continuous binary mask The convexhull of the biggestmask is computed as the final segmentationmask Figures 6(a) 6(b) 6(c) and 6(d) show the fingerprintsegmentation results by thresholding the biggest mask itsconvex hull and the final mask overlapped with the textureimage respectively

3 Experimental Results

In this section we conduct experiments on the NIST SD27latent fingerprint database to test the performances of theproposed algorithm NIST SD27 consists of 258 latent finger-prints which are manually assigned into three different levelsof qualities ldquogoodrdquo ldquobadrdquo and ldquouglyrdquo [18] The numbers ofldquogoodrdquo ldquobadrdquo and ldquouglyrdquo fingerprints are 88 85 and 85respectively Our algorithmwas implemented withMATLABsoftware on a computer of 31 GHz Intel Core i5-4440 4GRAM and 64-bit Windows OS For the latent fingerprintimages in NIST SD 27 the average computation time of ouralgorithm is about 5 seconds for one image The manualmarkups of ROI (Region of Interest) by experts providedin [19] are used as the ground truths for NIST SD 27 inour experiments We compare the segmentation results ofthe proposed algorithm with the manual markups and otherpublished methods [2ndash4 10]

First we test the proposed algorithm on the same samplelatent fingerprints of different image qualities from NISTSD27 database as those in [3] The segmentation resultsprovided in [3] are directly used for comparison The seg-mentation results of themethod [2] are downloaded from theauthorrsquos website Figures 7 8 and 9 show the comparisons ofthe segmentation results by themanual markups themethod[2] the method [3] and the proposed method for good badand ugly latent fingerprint images respectively From these

Table 1 Comparison of the segmentation accuracies in differentmethods

Methods MDR FDR AverageMethod [2] 1410 2613 2012Method [10] 1478 4799 3138Method [3] 922 187 1396Method [4] 1332 2421 1876Proposed method 1303 2317 1813

results we can see that the method [2] usually generated alarger segmentation mask which includes the nonfingerprintpatternsThere are some gaps and islands in the segmentationresults by themethod [3] Our proposedmethod can performwell to separate the fingerprint foreground from the complexbackground for latent images of different qualities which iscloser to the manual markups

Second although the segmentation results of latentimages can be evaluated visually we compare the segmenta-tion accuracy of our method with those of some publishedmethods [2ndash4 10] Twomeasures the Missed Detection Rate(MDR) and the False Detection Rate (FDR) are computedfor performance comparison on NIST SD27 The manualsegmentation masks are used as the ground truth MDRis the average percentage of foreground pixels misclassifiedas background while FDR is the average percentage ofbackground pixels misclassified as foreground Table 1 showsthe comparison of the segmentation accuracy in the proposedmethod with those of the existing methods [2ndash4 10] Thesegmentation results are directly used from those reportedin the paper In the comparison the computations of MDRand FDR and the ground truth of segmentation masks by theproposed method are same as those in [2 4 10] The MDRand FDR of our method are 1303 and 2317 respectivelyThese results show the effectiveness of our segmentationmethod compared to the other methods It seems that themethod [3] performed best in comparison But it should benoted that the segmentation ground truth in method [3] wasgenerated by the author of the paper and is different from ourproposed method and the computations of MDR and FDRmeasures were based on image patches instead of pixels

Security and Communication Networks 7

(a) (b) (c)

(d) (e) (f)

Figure 7 (a) Good latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

(a) (b) (c)

(d) (e) (f)

Figure 8 (a) Bad latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

8 Security and Communication Networks

(a) (b) (c)

(d) (e) (f)

Figure 9 (a) Ugly latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

The ultimate goal of segmentation is to improve theperformance of latent fingerprint identification Thus weconduct the latent fingerprint identification experiments totest the effectiveness of the proposed segmentation algorithmfinally The foreground of segmented texture image is theinput of latent image matching The commercial softwareVeriFinger SDK 65 is integrated to perform the featureextraction and matching for latent fingerprint identificationTo make the latent matching experiment more realistic andchallenging the template database includes both the 258 tem-plate fingerprints of NIST SD27 and the 27000 fingerprintsof NIST SD14 Each latent fingerprint is matched against allthe fingerprints of template database and a matching scoreis generated between the latent and a template fingerprintThe matching scores are sorted in descending order andthe identification performance is evaluated by CumulativeMatch Characteristic (CMC) curve A CMC curve plots theidentification accuracy rates with respect to different rank-k(119896 = 1 2 20)

We compare the performances of the proposed algorithmwith those of the linear density based method [4] andthe manual markup as well as those without segmenta-tion which are denoted as ldquolinear density based methodrdquoldquoproposed methodrdquo ldquomanual markuprdquo and ldquowithout seg-mentationrdquo respectively ldquoWithout segmentationrdquo means thetexture image is directly used as input for latent fingerprintidentification Figures 10(a) 10(b) 10(c) and 10(d) show the

comparisons of CMC curves on all latent ldquogoodrdquo ldquobadrdquoand ldquouglyrdquo quality fingerprints of NIST SD27 respectivelyWe can see that the identification of latent fingerprints withsegmentation can achieve much better performance thanthat without segmentation for all latent fingerprints Forbad latent images the proposed algorithm performs betterthan the linear density based method [4] and even betterthan the manual markup The performance is just slightlyworse than the manual markup for latent fingerprints ofgood quality For all fingerprints the proposed algorithmhas better performance than the linear density based method[4] and achieves comparable performance to the manualmarkup These results and comparison demonstrate theeffectiveness of the proposed segmentation algorithm forlatent fingerprints

4 Conclusion

This paper proposes a latent fingerprint segmentation algo-rithm based on combination of ridge density and orientationconsistency First a texture image is generated by imagedecomposition with a TV model and the structured noiseand illumination variation is greatly reduced from the textureimage Second we propose to detect the ridge segments andcompute the ridge density and orientation consistency whichare combined for latent fingerprint segmentation Finally theproposed algorithm is tested on NIST SD27 and compared

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

4 Security and Communication Networks

(a) (b) (c)

Figure 4 (a) Binary image with noise removal (b) the reversed binary image with noise removal and (c) the final detected ridge segmentpoints

The total variation (TV) model was proved to be an effi-cient model for regularizing images without smoothing theboundaries of targets [16] To reduce these structured noisesfor latent fingerprint segmentation the TV model is usedto decompose the latent image 119910 into texture and cartooncomponents ie 119910 = 119906 + V Texture component V containsthe oscillatory and noise component while cartoon 119906 consistsof the piecewise smooth component Mathematically thedecomposition is obtained by minimization of total variation[16]

min(119906V)isin1198712(Ω)

TV (119906) + 120582 1003817100381710038171003817119906 minus 119910100381710038171003817100381722 (1)

where 119906 and V denote the image cartoon and texture com-ponents respectively TV(119906) denotes the total variation of119906 and sdot 2 is a two norm The parameter 120582 isin [0 1] isused to balance the tradeoff between the fidelity of 119906 and theminimization of TV Small 120582 will produce weak texture andstrong cartoon image and vice versa In our experiments 120582 isset to 04 to decompose the texture component of latent imagefor fingerprint segmentation

Figures 3(b) and 3(c) show the decomposed cartoonand texture components of the latent image shown in Fig-ure 3(a) respectively The cartoon component consists ofmost nonfingerprint patterns and is removed as noise whilethe texture image contains the weak fingerprint patternAfter decomposition not only the structure noise but alsothe varying illuminations have been significantly reducedfrom the texture image which will facilitate latent fingerprintsegmentation

22 Detection of Ridge Segments After image decompositionby TV model a texture image is obtained and used forfurther processing while the cartoon image with most ofthe structured noises is removed as nonfingerprint patternIt will be easier to extract features from the texture imagefor fingerprint segmentation The texture component oflatent fingerprint is characterized by oscillatory patterns withinterleaved and parallel ridge and valley structure In thiswork we propose to detect the ridge segments from the

texture image to capture the fingerprint patterns The ridgesegment detection procedure can be divided into image bina-rization with noise removal image thinning and orientationcomputation for each ridge point detailed as follows

First the texture image is a gray scale image of [0 255]which is thresholded to a binary image with noise removalFor simplicity the threshold is set to 128 In the binary imagethe black points are the background pixels while the whitepoints are the candidate points of ridge segments Since thelatent fingerprint is often corrupted by various types of noiseit is necessary to remove the noisy points from the binaryimage formore reliable detection of ridge segmentsThenoisypoints usually occur as the isolated points and are removedaccording to the following formula

119868 (119894 119895) =

0 119868 (119894 minus 1 119895) + 119868 (119894 + 1 119895) = 00 119868 (119894 119895 minus 1) + 119868 (119894 119895 + 1) = 00 119868 (119894 minus 1 119895 minus 1) + 119868 (119894 + 1 119895 + 1) = 00 119868 (119894 + 1 119895 minus 1) + 119868 (119894 minus 1 119895 + 1) = 01 elsewise

(2)

Figure 4(a) shows the binary image after removing thenoisy points from the thresholded binary image of textureimage in Figure 3(c) In addition to enhance the detection ofridge segments we also reverse the thresholded binary imagewith the black points changed into white points and viceversaThe noisy points are removed with the same procedureas above These two binary images with noise removal aregenerated for better characterization of the interleaved ridgeand valley pattern Figure 4(b) shows the reversed binaryimage after removing the noisy points

Second the above two binary images are thinnedwith thethinning method in [17] and these two thinned images aresuperimposed into one binary image to form ridge segmentpoints However there are some noisy ridge segment pointswhich are usually characterized to be short closed andcrossed To remove the noisy ridge points for reliable detec-tion of ridge segments we delete the short ridge segments

Security and Communication Networks 5

(a) (b) (c)

Figure 5 (a) The ridge density map (b) orientation consistency map and (c) the final image quality map

with the length smaller than 4 and delete the closed ridgecircles with the number of end points less than 2 For eachridge point we compute the number of ridge points in its3 times 3 neighborhood If it is larger than 3 the point is thecrossed point and this point and all the ridge segment pointsin its 3 times 3 neighborhood are changed to black points Inthis way we go through all ridge points and obtain a set ofconnected and continuous ridge segments which are denotedas 1199031 1199032 119903119899 Figure 4(c) shows the final detected ridgesegment points from Figure 4(b)

Finally we compute the orientation for each ridge pointEach ridge segment is composed of a set of connectedpoints Thus the orientation of each ridge point is computedbased on its neighboring connected ridge points Assumethat the ridge segment 119903119896 consists of 119898 connected pointsdenoted as 1199011198960 1199011198961 119901119896119898 where 1199011198960 119901119896119896 and 119901119896119898 arethe starting middle and ending points of the ridge segment119903119896 respectively The orientation of point 119901119896119896 is computed asfollows

120579119901119896119896 =

arctan((119910119901119896119896+3 minus 119910119901119896119896)(119909119901119896119896+3 minus 119909119901119896119896)) 119896 + 3 le 119898arctan((119910119901119896119898 minus 119910119901119896119898minus3)(119909119901119896119898 minus 119909119901119896119898minus3)) 119896 + 3 gt 119898

(3)

where (119909119901119896119896 119910119901119896119896) is the coordinates of point 119901119896119896 Thus wehave obtained both the location and orientation for each ridgesegment point of the binary image

23 Computation of Ridge Density and Orientation Consis-tency From Figure 4(c) we can see that the valid fingerprintarea should have more parallel ridge segments than the non-fingerprint area Thus after detection of the ridge segmentsand computation of the orientation for each ridge point wepropose to compute a ridge density measure which evaluateshow densely the ridge points are distributed in a local regionwith the specific orientation In addition to the locationsthe orientations of ridge points are also considered becausethe neighboring ridge segments are parallel Thus the wholeorientation range [0 120587) is equally divided into 9 bins as1205731 1205732 1205739 ie 1205731 isin [0 1205879) 1205732 isin [1205879 21205879) 1205739 isin

[81205879 120587) To compute the ridge density we divide the binaryimage with the ridge segment points into the blocks of 16 times16 pixels Let 119878119903 denote the total number of ridge points ineach block and 119878120573119896119903 (119896 = 1 2 9) denote the number ofridge points in each block with the orientation in the bin 120573119896The interleaved ridge and valley flows of fingerprint changesmoothly except for a few singular points Thus the ridgepoints are densely distributed in a local fingerprint regionwith consistent orientationsThe ridge density for block (119894 119895)is computed by sum of the weighted distribution of the ridgepoints with respect to different orientations as follows

Rd (119894 119895) = 9sum119896=1

(119878120573119896119903119878 ) times (119878120573119896119903119878119903 ) (4)

where 119878 is the area of the block The ridge density Rd(119894 119895)is high if the ridge points are densely distributed in a blockand concentrated in one orientation bin Otherwise the ridgedensity is low

In addition the orientations of ridge segments usuallychange slowly in a local neighborhood The orientation ofeach block is defined as the orientation with the maximumnumber of ridge points We compute the orientation con-sistency to evaluate how consistent the orientation of eachblock is with its 8 neighboring orientations in the 3 times 3neighborhood The orientation consistency for block (119894 119895) iscomputed as

Cons (119894 119895)= sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 10038161003816100381610038161003816cos (120579119894+119898119895+119899 minus 120579119894119895)10038161003816100381610038161003816 minus 1sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 (5)

where 119908119894+119898119895+119899 isin 0 1 denotes the availability of orientation120579119894+119898119895+119899 of block (119894 + 119898 119895 + 119899) and Cons(119894 119895) isin [0 1] Ifthe block orientation is consistent with its 8 neighboringorientations Cons(119894 119895) is close to 1 Figures 5(a) and 5(b)show the ridge density map and the orientation consistencymap of Figure 4(c) respectively

24 Latent Fingerprint Segmentation For latent fingerprintsegmentation the ridge density and orientation consistency

6 Security and Communication Networks

(a) (b) (c) (d)

Figure 6 Fingerprint segmentation results by (a) thresholding (b) the biggest mask (c) the convex hull and (d) the final segmentation resultoverlapped with texture image

are combined to generate an image quality measure which iscomputed for each block as

119876 (119894 119895) = Rd (119894 119895) times Cons (119894 119895) (6)

Figure 5(c) shows the image quality map of Figure 4(c)Thresholding is applied to generate a binary segmentationmask for each fingerprint To fill the gaps and eliminatethe islands morphological operations are further applied toobtain the biggest and continuous binary mask The convexhull of the biggestmask is computed as the final segmentationmask Figures 6(a) 6(b) 6(c) and 6(d) show the fingerprintsegmentation results by thresholding the biggest mask itsconvex hull and the final mask overlapped with the textureimage respectively

3 Experimental Results

In this section we conduct experiments on the NIST SD27latent fingerprint database to test the performances of theproposed algorithm NIST SD27 consists of 258 latent finger-prints which are manually assigned into three different levelsof qualities ldquogoodrdquo ldquobadrdquo and ldquouglyrdquo [18] The numbers ofldquogoodrdquo ldquobadrdquo and ldquouglyrdquo fingerprints are 88 85 and 85respectively Our algorithmwas implemented withMATLABsoftware on a computer of 31 GHz Intel Core i5-4440 4GRAM and 64-bit Windows OS For the latent fingerprintimages in NIST SD 27 the average computation time of ouralgorithm is about 5 seconds for one image The manualmarkups of ROI (Region of Interest) by experts providedin [19] are used as the ground truths for NIST SD 27 inour experiments We compare the segmentation results ofthe proposed algorithm with the manual markups and otherpublished methods [2ndash4 10]

First we test the proposed algorithm on the same samplelatent fingerprints of different image qualities from NISTSD27 database as those in [3] The segmentation resultsprovided in [3] are directly used for comparison The seg-mentation results of themethod [2] are downloaded from theauthorrsquos website Figures 7 8 and 9 show the comparisons ofthe segmentation results by themanual markups themethod[2] the method [3] and the proposed method for good badand ugly latent fingerprint images respectively From these

Table 1 Comparison of the segmentation accuracies in differentmethods

Methods MDR FDR AverageMethod [2] 1410 2613 2012Method [10] 1478 4799 3138Method [3] 922 187 1396Method [4] 1332 2421 1876Proposed method 1303 2317 1813

results we can see that the method [2] usually generated alarger segmentation mask which includes the nonfingerprintpatternsThere are some gaps and islands in the segmentationresults by themethod [3] Our proposedmethod can performwell to separate the fingerprint foreground from the complexbackground for latent images of different qualities which iscloser to the manual markups

Second although the segmentation results of latentimages can be evaluated visually we compare the segmenta-tion accuracy of our method with those of some publishedmethods [2ndash4 10] Twomeasures the Missed Detection Rate(MDR) and the False Detection Rate (FDR) are computedfor performance comparison on NIST SD27 The manualsegmentation masks are used as the ground truth MDRis the average percentage of foreground pixels misclassifiedas background while FDR is the average percentage ofbackground pixels misclassified as foreground Table 1 showsthe comparison of the segmentation accuracy in the proposedmethod with those of the existing methods [2ndash4 10] Thesegmentation results are directly used from those reportedin the paper In the comparison the computations of MDRand FDR and the ground truth of segmentation masks by theproposed method are same as those in [2 4 10] The MDRand FDR of our method are 1303 and 2317 respectivelyThese results show the effectiveness of our segmentationmethod compared to the other methods It seems that themethod [3] performed best in comparison But it should benoted that the segmentation ground truth in method [3] wasgenerated by the author of the paper and is different from ourproposed method and the computations of MDR and FDRmeasures were based on image patches instead of pixels

Security and Communication Networks 7

(a) (b) (c)

(d) (e) (f)

Figure 7 (a) Good latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

(a) (b) (c)

(d) (e) (f)

Figure 8 (a) Bad latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

8 Security and Communication Networks

(a) (b) (c)

(d) (e) (f)

Figure 9 (a) Ugly latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

The ultimate goal of segmentation is to improve theperformance of latent fingerprint identification Thus weconduct the latent fingerprint identification experiments totest the effectiveness of the proposed segmentation algorithmfinally The foreground of segmented texture image is theinput of latent image matching The commercial softwareVeriFinger SDK 65 is integrated to perform the featureextraction and matching for latent fingerprint identificationTo make the latent matching experiment more realistic andchallenging the template database includes both the 258 tem-plate fingerprints of NIST SD27 and the 27000 fingerprintsof NIST SD14 Each latent fingerprint is matched against allthe fingerprints of template database and a matching scoreis generated between the latent and a template fingerprintThe matching scores are sorted in descending order andthe identification performance is evaluated by CumulativeMatch Characteristic (CMC) curve A CMC curve plots theidentification accuracy rates with respect to different rank-k(119896 = 1 2 20)

We compare the performances of the proposed algorithmwith those of the linear density based method [4] andthe manual markup as well as those without segmenta-tion which are denoted as ldquolinear density based methodrdquoldquoproposed methodrdquo ldquomanual markuprdquo and ldquowithout seg-mentationrdquo respectively ldquoWithout segmentationrdquo means thetexture image is directly used as input for latent fingerprintidentification Figures 10(a) 10(b) 10(c) and 10(d) show the

comparisons of CMC curves on all latent ldquogoodrdquo ldquobadrdquoand ldquouglyrdquo quality fingerprints of NIST SD27 respectivelyWe can see that the identification of latent fingerprints withsegmentation can achieve much better performance thanthat without segmentation for all latent fingerprints Forbad latent images the proposed algorithm performs betterthan the linear density based method [4] and even betterthan the manual markup The performance is just slightlyworse than the manual markup for latent fingerprints ofgood quality For all fingerprints the proposed algorithmhas better performance than the linear density based method[4] and achieves comparable performance to the manualmarkup These results and comparison demonstrate theeffectiveness of the proposed segmentation algorithm forlatent fingerprints

4 Conclusion

This paper proposes a latent fingerprint segmentation algo-rithm based on combination of ridge density and orientationconsistency First a texture image is generated by imagedecomposition with a TV model and the structured noiseand illumination variation is greatly reduced from the textureimage Second we propose to detect the ridge segments andcompute the ridge density and orientation consistency whichare combined for latent fingerprint segmentation Finally theproposed algorithm is tested on NIST SD27 and compared

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

Security and Communication Networks 5

(a) (b) (c)

Figure 5 (a) The ridge density map (b) orientation consistency map and (c) the final image quality map

with the length smaller than 4 and delete the closed ridgecircles with the number of end points less than 2 For eachridge point we compute the number of ridge points in its3 times 3 neighborhood If it is larger than 3 the point is thecrossed point and this point and all the ridge segment pointsin its 3 times 3 neighborhood are changed to black points Inthis way we go through all ridge points and obtain a set ofconnected and continuous ridge segments which are denotedas 1199031 1199032 119903119899 Figure 4(c) shows the final detected ridgesegment points from Figure 4(b)

Finally we compute the orientation for each ridge pointEach ridge segment is composed of a set of connectedpoints Thus the orientation of each ridge point is computedbased on its neighboring connected ridge points Assumethat the ridge segment 119903119896 consists of 119898 connected pointsdenoted as 1199011198960 1199011198961 119901119896119898 where 1199011198960 119901119896119896 and 119901119896119898 arethe starting middle and ending points of the ridge segment119903119896 respectively The orientation of point 119901119896119896 is computed asfollows

120579119901119896119896 =

arctan((119910119901119896119896+3 minus 119910119901119896119896)(119909119901119896119896+3 minus 119909119901119896119896)) 119896 + 3 le 119898arctan((119910119901119896119898 minus 119910119901119896119898minus3)(119909119901119896119898 minus 119909119901119896119898minus3)) 119896 + 3 gt 119898

(3)

where (119909119901119896119896 119910119901119896119896) is the coordinates of point 119901119896119896 Thus wehave obtained both the location and orientation for each ridgesegment point of the binary image

23 Computation of Ridge Density and Orientation Consis-tency From Figure 4(c) we can see that the valid fingerprintarea should have more parallel ridge segments than the non-fingerprint area Thus after detection of the ridge segmentsand computation of the orientation for each ridge point wepropose to compute a ridge density measure which evaluateshow densely the ridge points are distributed in a local regionwith the specific orientation In addition to the locationsthe orientations of ridge points are also considered becausethe neighboring ridge segments are parallel Thus the wholeorientation range [0 120587) is equally divided into 9 bins as1205731 1205732 1205739 ie 1205731 isin [0 1205879) 1205732 isin [1205879 21205879) 1205739 isin

[81205879 120587) To compute the ridge density we divide the binaryimage with the ridge segment points into the blocks of 16 times16 pixels Let 119878119903 denote the total number of ridge points ineach block and 119878120573119896119903 (119896 = 1 2 9) denote the number ofridge points in each block with the orientation in the bin 120573119896The interleaved ridge and valley flows of fingerprint changesmoothly except for a few singular points Thus the ridgepoints are densely distributed in a local fingerprint regionwith consistent orientationsThe ridge density for block (119894 119895)is computed by sum of the weighted distribution of the ridgepoints with respect to different orientations as follows

Rd (119894 119895) = 9sum119896=1

(119878120573119896119903119878 ) times (119878120573119896119903119878119903 ) (4)

where 119878 is the area of the block The ridge density Rd(119894 119895)is high if the ridge points are densely distributed in a blockand concentrated in one orientation bin Otherwise the ridgedensity is low

In addition the orientations of ridge segments usuallychange slowly in a local neighborhood The orientation ofeach block is defined as the orientation with the maximumnumber of ridge points We compute the orientation con-sistency to evaluate how consistent the orientation of eachblock is with its 8 neighboring orientations in the 3 times 3neighborhood The orientation consistency for block (119894 119895) iscomputed as

Cons (119894 119895)= sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 10038161003816100381610038161003816cos (120579119894+119898119895+119899 minus 120579119894119895)10038161003816100381610038161003816 minus 1sum1119898=minus1sum1119899=minus1 119908119894+119898119895+119899 (5)

where 119908119894+119898119895+119899 isin 0 1 denotes the availability of orientation120579119894+119898119895+119899 of block (119894 + 119898 119895 + 119899) and Cons(119894 119895) isin [0 1] Ifthe block orientation is consistent with its 8 neighboringorientations Cons(119894 119895) is close to 1 Figures 5(a) and 5(b)show the ridge density map and the orientation consistencymap of Figure 4(c) respectively

24 Latent Fingerprint Segmentation For latent fingerprintsegmentation the ridge density and orientation consistency

6 Security and Communication Networks

(a) (b) (c) (d)

Figure 6 Fingerprint segmentation results by (a) thresholding (b) the biggest mask (c) the convex hull and (d) the final segmentation resultoverlapped with texture image

are combined to generate an image quality measure which iscomputed for each block as

119876 (119894 119895) = Rd (119894 119895) times Cons (119894 119895) (6)

Figure 5(c) shows the image quality map of Figure 4(c)Thresholding is applied to generate a binary segmentationmask for each fingerprint To fill the gaps and eliminatethe islands morphological operations are further applied toobtain the biggest and continuous binary mask The convexhull of the biggestmask is computed as the final segmentationmask Figures 6(a) 6(b) 6(c) and 6(d) show the fingerprintsegmentation results by thresholding the biggest mask itsconvex hull and the final mask overlapped with the textureimage respectively

3 Experimental Results

In this section we conduct experiments on the NIST SD27latent fingerprint database to test the performances of theproposed algorithm NIST SD27 consists of 258 latent finger-prints which are manually assigned into three different levelsof qualities ldquogoodrdquo ldquobadrdquo and ldquouglyrdquo [18] The numbers ofldquogoodrdquo ldquobadrdquo and ldquouglyrdquo fingerprints are 88 85 and 85respectively Our algorithmwas implemented withMATLABsoftware on a computer of 31 GHz Intel Core i5-4440 4GRAM and 64-bit Windows OS For the latent fingerprintimages in NIST SD 27 the average computation time of ouralgorithm is about 5 seconds for one image The manualmarkups of ROI (Region of Interest) by experts providedin [19] are used as the ground truths for NIST SD 27 inour experiments We compare the segmentation results ofthe proposed algorithm with the manual markups and otherpublished methods [2ndash4 10]

First we test the proposed algorithm on the same samplelatent fingerprints of different image qualities from NISTSD27 database as those in [3] The segmentation resultsprovided in [3] are directly used for comparison The seg-mentation results of themethod [2] are downloaded from theauthorrsquos website Figures 7 8 and 9 show the comparisons ofthe segmentation results by themanual markups themethod[2] the method [3] and the proposed method for good badand ugly latent fingerprint images respectively From these

Table 1 Comparison of the segmentation accuracies in differentmethods

Methods MDR FDR AverageMethod [2] 1410 2613 2012Method [10] 1478 4799 3138Method [3] 922 187 1396Method [4] 1332 2421 1876Proposed method 1303 2317 1813

results we can see that the method [2] usually generated alarger segmentation mask which includes the nonfingerprintpatternsThere are some gaps and islands in the segmentationresults by themethod [3] Our proposedmethod can performwell to separate the fingerprint foreground from the complexbackground for latent images of different qualities which iscloser to the manual markups

Second although the segmentation results of latentimages can be evaluated visually we compare the segmenta-tion accuracy of our method with those of some publishedmethods [2ndash4 10] Twomeasures the Missed Detection Rate(MDR) and the False Detection Rate (FDR) are computedfor performance comparison on NIST SD27 The manualsegmentation masks are used as the ground truth MDRis the average percentage of foreground pixels misclassifiedas background while FDR is the average percentage ofbackground pixels misclassified as foreground Table 1 showsthe comparison of the segmentation accuracy in the proposedmethod with those of the existing methods [2ndash4 10] Thesegmentation results are directly used from those reportedin the paper In the comparison the computations of MDRand FDR and the ground truth of segmentation masks by theproposed method are same as those in [2 4 10] The MDRand FDR of our method are 1303 and 2317 respectivelyThese results show the effectiveness of our segmentationmethod compared to the other methods It seems that themethod [3] performed best in comparison But it should benoted that the segmentation ground truth in method [3] wasgenerated by the author of the paper and is different from ourproposed method and the computations of MDR and FDRmeasures were based on image patches instead of pixels

Security and Communication Networks 7

(a) (b) (c)

(d) (e) (f)

Figure 7 (a) Good latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

(a) (b) (c)

(d) (e) (f)

Figure 8 (a) Bad latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

8 Security and Communication Networks

(a) (b) (c)

(d) (e) (f)

Figure 9 (a) Ugly latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

The ultimate goal of segmentation is to improve theperformance of latent fingerprint identification Thus weconduct the latent fingerprint identification experiments totest the effectiveness of the proposed segmentation algorithmfinally The foreground of segmented texture image is theinput of latent image matching The commercial softwareVeriFinger SDK 65 is integrated to perform the featureextraction and matching for latent fingerprint identificationTo make the latent matching experiment more realistic andchallenging the template database includes both the 258 tem-plate fingerprints of NIST SD27 and the 27000 fingerprintsof NIST SD14 Each latent fingerprint is matched against allthe fingerprints of template database and a matching scoreis generated between the latent and a template fingerprintThe matching scores are sorted in descending order andthe identification performance is evaluated by CumulativeMatch Characteristic (CMC) curve A CMC curve plots theidentification accuracy rates with respect to different rank-k(119896 = 1 2 20)

We compare the performances of the proposed algorithmwith those of the linear density based method [4] andthe manual markup as well as those without segmenta-tion which are denoted as ldquolinear density based methodrdquoldquoproposed methodrdquo ldquomanual markuprdquo and ldquowithout seg-mentationrdquo respectively ldquoWithout segmentationrdquo means thetexture image is directly used as input for latent fingerprintidentification Figures 10(a) 10(b) 10(c) and 10(d) show the

comparisons of CMC curves on all latent ldquogoodrdquo ldquobadrdquoand ldquouglyrdquo quality fingerprints of NIST SD27 respectivelyWe can see that the identification of latent fingerprints withsegmentation can achieve much better performance thanthat without segmentation for all latent fingerprints Forbad latent images the proposed algorithm performs betterthan the linear density based method [4] and even betterthan the manual markup The performance is just slightlyworse than the manual markup for latent fingerprints ofgood quality For all fingerprints the proposed algorithmhas better performance than the linear density based method[4] and achieves comparable performance to the manualmarkup These results and comparison demonstrate theeffectiveness of the proposed segmentation algorithm forlatent fingerprints

4 Conclusion

This paper proposes a latent fingerprint segmentation algo-rithm based on combination of ridge density and orientationconsistency First a texture image is generated by imagedecomposition with a TV model and the structured noiseand illumination variation is greatly reduced from the textureimage Second we propose to detect the ridge segments andcompute the ridge density and orientation consistency whichare combined for latent fingerprint segmentation Finally theproposed algorithm is tested on NIST SD27 and compared

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

6 Security and Communication Networks

(a) (b) (c) (d)

Figure 6 Fingerprint segmentation results by (a) thresholding (b) the biggest mask (c) the convex hull and (d) the final segmentation resultoverlapped with texture image

are combined to generate an image quality measure which iscomputed for each block as

119876 (119894 119895) = Rd (119894 119895) times Cons (119894 119895) (6)

Figure 5(c) shows the image quality map of Figure 4(c)Thresholding is applied to generate a binary segmentationmask for each fingerprint To fill the gaps and eliminatethe islands morphological operations are further applied toobtain the biggest and continuous binary mask The convexhull of the biggestmask is computed as the final segmentationmask Figures 6(a) 6(b) 6(c) and 6(d) show the fingerprintsegmentation results by thresholding the biggest mask itsconvex hull and the final mask overlapped with the textureimage respectively

3 Experimental Results

In this section we conduct experiments on the NIST SD27latent fingerprint database to test the performances of theproposed algorithm NIST SD27 consists of 258 latent finger-prints which are manually assigned into three different levelsof qualities ldquogoodrdquo ldquobadrdquo and ldquouglyrdquo [18] The numbers ofldquogoodrdquo ldquobadrdquo and ldquouglyrdquo fingerprints are 88 85 and 85respectively Our algorithmwas implemented withMATLABsoftware on a computer of 31 GHz Intel Core i5-4440 4GRAM and 64-bit Windows OS For the latent fingerprintimages in NIST SD 27 the average computation time of ouralgorithm is about 5 seconds for one image The manualmarkups of ROI (Region of Interest) by experts providedin [19] are used as the ground truths for NIST SD 27 inour experiments We compare the segmentation results ofthe proposed algorithm with the manual markups and otherpublished methods [2ndash4 10]

First we test the proposed algorithm on the same samplelatent fingerprints of different image qualities from NISTSD27 database as those in [3] The segmentation resultsprovided in [3] are directly used for comparison The seg-mentation results of themethod [2] are downloaded from theauthorrsquos website Figures 7 8 and 9 show the comparisons ofthe segmentation results by themanual markups themethod[2] the method [3] and the proposed method for good badand ugly latent fingerprint images respectively From these

Table 1 Comparison of the segmentation accuracies in differentmethods

Methods MDR FDR AverageMethod [2] 1410 2613 2012Method [10] 1478 4799 3138Method [3] 922 187 1396Method [4] 1332 2421 1876Proposed method 1303 2317 1813

results we can see that the method [2] usually generated alarger segmentation mask which includes the nonfingerprintpatternsThere are some gaps and islands in the segmentationresults by themethod [3] Our proposedmethod can performwell to separate the fingerprint foreground from the complexbackground for latent images of different qualities which iscloser to the manual markups

Second although the segmentation results of latentimages can be evaluated visually we compare the segmenta-tion accuracy of our method with those of some publishedmethods [2ndash4 10] Twomeasures the Missed Detection Rate(MDR) and the False Detection Rate (FDR) are computedfor performance comparison on NIST SD27 The manualsegmentation masks are used as the ground truth MDRis the average percentage of foreground pixels misclassifiedas background while FDR is the average percentage ofbackground pixels misclassified as foreground Table 1 showsthe comparison of the segmentation accuracy in the proposedmethod with those of the existing methods [2ndash4 10] Thesegmentation results are directly used from those reportedin the paper In the comparison the computations of MDRand FDR and the ground truth of segmentation masks by theproposed method are same as those in [2 4 10] The MDRand FDR of our method are 1303 and 2317 respectivelyThese results show the effectiveness of our segmentationmethod compared to the other methods It seems that themethod [3] performed best in comparison But it should benoted that the segmentation ground truth in method [3] wasgenerated by the author of the paper and is different from ourproposed method and the computations of MDR and FDRmeasures were based on image patches instead of pixels

Security and Communication Networks 7

(a) (b) (c)

(d) (e) (f)

Figure 7 (a) Good latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

(a) (b) (c)

(d) (e) (f)

Figure 8 (a) Bad latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

8 Security and Communication Networks

(a) (b) (c)

(d) (e) (f)

Figure 9 (a) Ugly latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

The ultimate goal of segmentation is to improve theperformance of latent fingerprint identification Thus weconduct the latent fingerprint identification experiments totest the effectiveness of the proposed segmentation algorithmfinally The foreground of segmented texture image is theinput of latent image matching The commercial softwareVeriFinger SDK 65 is integrated to perform the featureextraction and matching for latent fingerprint identificationTo make the latent matching experiment more realistic andchallenging the template database includes both the 258 tem-plate fingerprints of NIST SD27 and the 27000 fingerprintsof NIST SD14 Each latent fingerprint is matched against allthe fingerprints of template database and a matching scoreis generated between the latent and a template fingerprintThe matching scores are sorted in descending order andthe identification performance is evaluated by CumulativeMatch Characteristic (CMC) curve A CMC curve plots theidentification accuracy rates with respect to different rank-k(119896 = 1 2 20)

We compare the performances of the proposed algorithmwith those of the linear density based method [4] andthe manual markup as well as those without segmenta-tion which are denoted as ldquolinear density based methodrdquoldquoproposed methodrdquo ldquomanual markuprdquo and ldquowithout seg-mentationrdquo respectively ldquoWithout segmentationrdquo means thetexture image is directly used as input for latent fingerprintidentification Figures 10(a) 10(b) 10(c) and 10(d) show the

comparisons of CMC curves on all latent ldquogoodrdquo ldquobadrdquoand ldquouglyrdquo quality fingerprints of NIST SD27 respectivelyWe can see that the identification of latent fingerprints withsegmentation can achieve much better performance thanthat without segmentation for all latent fingerprints Forbad latent images the proposed algorithm performs betterthan the linear density based method [4] and even betterthan the manual markup The performance is just slightlyworse than the manual markup for latent fingerprints ofgood quality For all fingerprints the proposed algorithmhas better performance than the linear density based method[4] and achieves comparable performance to the manualmarkup These results and comparison demonstrate theeffectiveness of the proposed segmentation algorithm forlatent fingerprints

4 Conclusion

This paper proposes a latent fingerprint segmentation algo-rithm based on combination of ridge density and orientationconsistency First a texture image is generated by imagedecomposition with a TV model and the structured noiseand illumination variation is greatly reduced from the textureimage Second we propose to detect the ridge segments andcompute the ridge density and orientation consistency whichare combined for latent fingerprint segmentation Finally theproposed algorithm is tested on NIST SD27 and compared

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

Security and Communication Networks 7

(a) (b) (c)

(d) (e) (f)

Figure 7 (a) Good latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

(a) (b) (c)

(d) (e) (f)

Figure 8 (a) Bad latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

8 Security and Communication Networks

(a) (b) (c)

(d) (e) (f)

Figure 9 (a) Ugly latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

The ultimate goal of segmentation is to improve theperformance of latent fingerprint identification Thus weconduct the latent fingerprint identification experiments totest the effectiveness of the proposed segmentation algorithmfinally The foreground of segmented texture image is theinput of latent image matching The commercial softwareVeriFinger SDK 65 is integrated to perform the featureextraction and matching for latent fingerprint identificationTo make the latent matching experiment more realistic andchallenging the template database includes both the 258 tem-plate fingerprints of NIST SD27 and the 27000 fingerprintsof NIST SD14 Each latent fingerprint is matched against allthe fingerprints of template database and a matching scoreis generated between the latent and a template fingerprintThe matching scores are sorted in descending order andthe identification performance is evaluated by CumulativeMatch Characteristic (CMC) curve A CMC curve plots theidentification accuracy rates with respect to different rank-k(119896 = 1 2 20)

We compare the performances of the proposed algorithmwith those of the linear density based method [4] andthe manual markup as well as those without segmenta-tion which are denoted as ldquolinear density based methodrdquoldquoproposed methodrdquo ldquomanual markuprdquo and ldquowithout seg-mentationrdquo respectively ldquoWithout segmentationrdquo means thetexture image is directly used as input for latent fingerprintidentification Figures 10(a) 10(b) 10(c) and 10(d) show the

comparisons of CMC curves on all latent ldquogoodrdquo ldquobadrdquoand ldquouglyrdquo quality fingerprints of NIST SD27 respectivelyWe can see that the identification of latent fingerprints withsegmentation can achieve much better performance thanthat without segmentation for all latent fingerprints Forbad latent images the proposed algorithm performs betterthan the linear density based method [4] and even betterthan the manual markup The performance is just slightlyworse than the manual markup for latent fingerprints ofgood quality For all fingerprints the proposed algorithmhas better performance than the linear density based method[4] and achieves comparable performance to the manualmarkup These results and comparison demonstrate theeffectiveness of the proposed segmentation algorithm forlatent fingerprints

4 Conclusion

This paper proposes a latent fingerprint segmentation algo-rithm based on combination of ridge density and orientationconsistency First a texture image is generated by imagedecomposition with a TV model and the structured noiseand illumination variation is greatly reduced from the textureimage Second we propose to detect the ridge segments andcompute the ridge density and orientation consistency whichare combined for latent fingerprint segmentation Finally theproposed algorithm is tested on NIST SD27 and compared

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

8 Security and Communication Networks

(a) (b) (c)

(d) (e) (f)

Figure 9 (a) Ugly latent fingerprint image (b) the texture image (c) manual marked ROI and the segmentation results (d) by method [2]method [3] and our proposed algorithm

The ultimate goal of segmentation is to improve theperformance of latent fingerprint identification Thus weconduct the latent fingerprint identification experiments totest the effectiveness of the proposed segmentation algorithmfinally The foreground of segmented texture image is theinput of latent image matching The commercial softwareVeriFinger SDK 65 is integrated to perform the featureextraction and matching for latent fingerprint identificationTo make the latent matching experiment more realistic andchallenging the template database includes both the 258 tem-plate fingerprints of NIST SD27 and the 27000 fingerprintsof NIST SD14 Each latent fingerprint is matched against allthe fingerprints of template database and a matching scoreis generated between the latent and a template fingerprintThe matching scores are sorted in descending order andthe identification performance is evaluated by CumulativeMatch Characteristic (CMC) curve A CMC curve plots theidentification accuracy rates with respect to different rank-k(119896 = 1 2 20)

We compare the performances of the proposed algorithmwith those of the linear density based method [4] andthe manual markup as well as those without segmenta-tion which are denoted as ldquolinear density based methodrdquoldquoproposed methodrdquo ldquomanual markuprdquo and ldquowithout seg-mentationrdquo respectively ldquoWithout segmentationrdquo means thetexture image is directly used as input for latent fingerprintidentification Figures 10(a) 10(b) 10(c) and 10(d) show the

comparisons of CMC curves on all latent ldquogoodrdquo ldquobadrdquoand ldquouglyrdquo quality fingerprints of NIST SD27 respectivelyWe can see that the identification of latent fingerprints withsegmentation can achieve much better performance thanthat without segmentation for all latent fingerprints Forbad latent images the proposed algorithm performs betterthan the linear density based method [4] and even betterthan the manual markup The performance is just slightlyworse than the manual markup for latent fingerprints ofgood quality For all fingerprints the proposed algorithmhas better performance than the linear density based method[4] and achieves comparable performance to the manualmarkup These results and comparison demonstrate theeffectiveness of the proposed segmentation algorithm forlatent fingerprints

4 Conclusion

This paper proposes a latent fingerprint segmentation algo-rithm based on combination of ridge density and orientationconsistency First a texture image is generated by imagedecomposition with a TV model and the structured noiseand illumination variation is greatly reduced from the textureimage Second we propose to detect the ridge segments andcompute the ridge density and orientation consistency whichare combined for latent fingerprint segmentation Finally theproposed algorithm is tested on NIST SD27 and compared

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

Security and Communication Networks 9

(a) (b)

(c) (d)

Figure 10 Comparisons of CMC curves by the proposed method the linear density based method [4] and the manual markup as well astexture image without segmentation for (a) all (b) good (c) bad and (d) ugly latent fingerprints of NIST SD27

with other methods and the manual markups Experimentalresults and comparisons demonstrate the effectiveness of theproposed segmentation algorithm for latent fingerprints

Conflicts of Interest

There are no conflicts of interest related to this paper

Acknowledgments

This work was supported in part by National Natural ScienceFoundation ofChina (NSFC) underGrants nos 61773263 and61375112 and The National Key Research and DevelopmentProgram of China (no 2016YFC0100903) SMC ExcellentYoung Faculty program of SJTU This work was also sup-ported in part by Science andTechnology of Fujian EducationDepartment under Grant no JAT170365

References

[1] A K Jain and J Feng ldquoLatent fingerprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol33 no 1 pp 88ndash100 2011

[2] J Zhang R Lai and C-C Jay Kuo ldquoAdaptive directionaltotal-variationmodel for latent fingerprint segmentationrdquo IEEETransactions on Information Forensics and Security vol 8 no 8pp 1261ndash1273 2013

[3] J Ezeobiejesi and B Bhanu ldquoLatent Fingerprint Image Segmen-tationUsing FractalDimensionFeatures andWeightedExtremeLearning Machine Ensemblerdquo in Proceedings of the 29th IEEEConference on Computer Vision and Pattern Recognition Work-shops CVPRW 2016 pp 214ndash222 2016

[4] S Liu M Liu and Z Yang ldquoLatent fingerprint segmentationbased on linear densityrdquo in Proceedings of the 9th IAPR Interna-tional Conference on Biometrics ICB 2016 2016

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

10 Security and Communication Networks

[5] M U Akram S Nasir A Tariq I Zafar andW Siddique KhanldquoImproved fingerprint image segmentation using newmodifiedgradient based techniquerdquo in Proceedings of the 2008 CanadianConference on Electrical and Computer Engineering - CCECErsquo08pp 001967ndash001972 Niagara Falls Canada 2008

[6] X Dai J Liang Q Zhao and F Liu ldquoFingerprint Segmentationvia Convolutional Neural Networksrdquo in Biometric Recognitionvol 10568 of Lecture Notes in Computer Science pp 324ndash333Springer International Publishing 2017

[7] A M Bazen and S H Gerez ldquoSegmentation of fingerprintimagesrdquo in Proceedings of 12th Annual Workshop CircuitsSystems and Signal Processing 2001

[8] J Ma X Jing Y Zhang S Sun and H Huang ldquoSimpleeffective fingerprint segmentation algorithm for low qualityimagesrdquo in Proceedings of the 2010 3rd IEEE InternationalConference on Broadband Network and Multimedia TechnologyIC-BNMT2010 pp 855ndash859 2010

[9] N Short M Hsiao A Abbott and E Fox ldquoLatent fingerprintsegmentation using ridge template correlationrdquo in Proceedingsof the 4th International Conference on Imaging for Crime Detec-tion and Prevention 2011 (ICDP 2011) pp P28ndashP28 LondonUK

[10] H Choi M Boaventura I A G Boaventura and A K JainldquoAutomatic segmentation of latent fingerprintsrdquo in Proceedingsof the 2012 IEEE 5th International Conference on BiometricsTheory Applications and Systems BTAS 2012 pp 303ndash310 usaSeptember 2012

[11] S Karimi-Ashtiani and C-C Jay Kuo ldquoA robust technique forlatent fingerprint image segmentation and enhancementrdquo inProceedings of the 2008 IEEE International Conference on ImageProcessing ICIP 2008 pp 1492ndash1495 usa October 2008

[12] K Cao E Liu and A K Jain ldquoSegmentation and enhancementof latent fingerprints a coarse to fine ridge structure dictionaryrdquoIEEE Transactions on Pattern Analysis andMachine Intelligencevol 36 no 9 pp 1847ndash1859 2014

[13] I Arshad G Raja and A K Khan ldquoLatent Fingerprints Seg-mentation Feasibility of Using Clustering-Based AutomatedApproachrdquo Arabian Journal for Science and Engineering vol 39no 11 pp 7933ndash7944 2014

[14] T Revathy G Pramila A Adhiraja and A Askerunisa ldquoAuto-matic Latent Fingerprint Segmentation based on Orientationand Frequency Featuresrdquo in Proceedings of the 3rd InternationalConference on Communication and Signal Processing ICCSP2014 pp 1192ndash1196 2014

[15] J Ezeobiejesi and B Bhanu ldquoLatent fingerprint image segmen-tation using deep neural networkrdquoAdvances in Computer Visionand Pattern Recognition vol 1 pp 83ndash107 2017

[16] A Buades T M Le J-M Morel and L A Vese ldquoFast cartoon+ texture image filtersrdquo IEEE Transactions on Image Processingvol 19 no 8 pp 1978ndash1986 2010

[17] L Lam and C Y Suen ldquoThinning methodologiesmdasha com-prehensive surveyrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 14 no 9 pp 869ndash885 1992

[18] NIST Special Database 27 ldquoFingerprint Minutiae fromLatent and Matching Ten-print Imagesrdquo httpwwwnistgovsrdnistsd27htm

[19] J Feng J Zhou and A K Jain ldquoOrientation field estimation forlatent fingerprint enhancementrdquo IEEE Transactions on PatternAnalysis and Machine Intelligence vol 35 no 4 pp 925ndash9402013

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