fingerprint enhancement by adaptive directional filtering · identification by adaptive directional...

EE5359 Multimedia Processing - Spring 2015 1

Fingerprint Enhancement and Identification by Adaptive

Directional Filtering

EE5359 MULTIMEDIA PROCESSING

SPRING 2015

Under the guidance of Dr. K. R. Rao

Presented byVishwak R Tadisina

ID:1001051048


Acronyms• 1D- One Dimension

• 2D- Two Dimension

• AFIS – Automatic Fingerprint Identification System

• DC- Direct Current

• ECTI-CON - Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology Conference

• FBI – Federal Bureau of Investigation

• FFT- Fast Fourier Transform

• ICBA- International Conference on Bioinformatics and its Applications

• ICPR – International Conference on Pattern Recognition

• IEE – Institution of Electrical Engineers

• IEEE- Institute of Electrical and Electronics Engineers

• ISCV – International symposium on Computer Vision

• LCNS- Lecture Notes in Computer Science

• LPF- Low Pass Filter

• MATLAB – Matrix Laboratory

• MTF – Modulation Transfer Function

• WACV- Winter Conference on Applications of Computer Vision

Project Interim- Fingerprint Enhancement and identification by Adaptive Directional Filtering


Introduction

• Identifying a person based on the biometrics has become important in current diverse businesses like law enforcement, information system security, finance physical access control etc. [4].

• Fingerprint recognition is one of the most important biometric technologies which has drawn a substantial amount of attention recently [4].

• The best aspect of fingerprint-based identification is that the fingerprints of a person are unique and does not alter with aging of an individual [1]

• A method to manually match fingerprint was developed by law enforcement agencies [4]. But this method is tedious and time taking.

• Automatic fingerprint identification system (AFIS)

• Input can be given by digitalizing the image take by ink or by using inkless scanners.



Stages in AFIS


Fig.1 Different stages involved in an Automatic fingerprint identification system [11].


Suitable features for representation of a fingerprint

• Keep back the uniqueness of each fingerprint in various levels of resolution.

• Distinct characteristics of a fingerprint can be estimated easily.

• Easy to apply automatic matching algorithms.

• Immune to noise distortions.

• Effective and simple representation [11].



Fingerprint Structure• Fingerprint is the image of the surface of the skin of the fingertip.

• It consists of ridges and valleys as shown in Fig.2.

• The ridge pattern in a fingerprint can be described as an oriented texture pattern with fixed dominant spatial frequency and orientation in a local neighbourhood [2].

• Orientation - flow pattern of the ridges [2].

• Frequency - inter-ridge spacing [2].

• The anomalies in a fingerprint are called minutiae (ex: ridge endings, bifurcations, crossovers, short ridges etc. as shown in Fig.2


Fig. 2 Bifurcations and short ridges in a fingerprint structure [11].


Fingerprint Enhancement Algorithm

• An ideal algorithm must increase the contrast between the ridges and valleys of a fingerprint for visual examination or automatic feature extraction [2].

• In this algorithm [2] during the processing of each pixel a local neighbourhood of that pixel is considered and this can be explained using Fig. 3.

• As the ridges and valleys have well-defined frequency and orientation in the local area directional filters are used [2].

• The filtering process is adaptive as the parameters of these directional filters depend on the localridge frequency and orientation [2].



Determining minutiae based on neighbouring pixels


Fig.3 In (a) the pixel with three neighbours is a ridge bifurcation and in (b), pixel with only one neighbour is a ridge ending [15].


Steps involved in the Fingerprint Enhancement Algorithm

• Normalization: To obtain a pre-specified mean and variance, an input fingerprint image is normalized [2]

• Local orientation and Frequency estimation: The normalized input fingerprint image is used forcomputing orientation and frequency images [2].

• Region mask estimation: Each block in the normalized input fingerprint image are sorted out intoa recoverable or an unrecoverable block to find a region mask estimate [2].

• Filtering: A bank of Gabor filters or Butterworth filters that are tuned to local ridge orientation and ridge frequency are used [2].



Flowchart of a fingerprint enhancement algorithm


Fig.4 Flowchart of a fingerprint enhancement algorithm [4].


Normalization

• Normalization reduces the variations in grey-level values along ridges and valleys [2]

• I(x, y) denote the grey-level value at pixel (x, y)

• Mi and Vi denote the estimated mean and variance of I

• M0 and V0 are the desired mean and variance values

• Ni(x, y) denote the normalized grey-level value at pixel (x, y)


(1)


Fingerprint after normalization


Fig. 5 The result of normalization. (a) Input image. (b) Normalized image [4].


Local Ridge Orientation• Local ridge orientation is usually specified blockwise rather than at every pixel [4].

• Least mean square orientation estimation based on gradient is used here [4].

• Each fingerprint image in divided into equal blocks and gradients are calculated for each pixel in a block and average squared gradient for the block is calculated from this [4].

• The average gradient ϕ direction and dominant local orientation O [1] for the block are given by:

• Correction for 90 degrees is necessary since the angle of gradient is perpendicular to the ridge orientation [4]. Here blocks of size W × W = 8×8 for orientation estimation and gradients gx and gy

are used and calculated using Sobel operator [2].


(2)

(3)


Local Ridge Orientation• Additional smoothing (Low pass filtering) is required at distorted and noisy regions [4].It is done

by converting orientation image into a continuous vector field as shown in the Fig. 8, defined as follows

• Where Ψx i, j and Ψy i, j are the x and y components of the continuous vector field respectively.


(4)

(5)

Fig.6 A continuous vector field formed by a local orientation image with a block of size W x W and center O (i, j).


Local Ridge Orientation

• The filter implementation [1] is given by,

• where L is a 2D LPF and WΨ ×WΨ specifies the size of the filter Ψx′ i, j and Ψy

′ i, j are the x and y components of the continuous vector field respectively after smoothing.


(6)

(7)

(8)


Local Ridge Frequency

• Local ridge frequency is found by projecting the grey values of all the pixels located in each block along a direction orthogonal to the local orientation. 1D wave with the local extrema corresponding to the ridges and valleys of the fingerprint [4];

• Let K(i, j) be the average number of pixels between two consecutive peaks in the 1D wave generated above. The frequency 𝜔 i, j [4] is computed as

𝜔 i, j =1/K(i, j) (4)

• In order to explain the above estimation a one dimensional (1D) modeled fingerprint image instead of the original raw fingerprint images can be used.

• A finite rectangular wave (as seen in Fig. 7) which is regarded as the simplification of the projection of all grey values of the pixels in a direction, normal to the local orientation of the block with local extrema corresponding to the ridges and valleys of the fingerprint.



Finite rectangular wave as a modeled fingerprint


Fig. 7 Finite rectangular wave as a modeled fingerprint [15].


Directional Filter in Fourier Domain


Fig. 8 Filter in Fourier domain (a) band pass (radial) component, (b) directional (angular) component, (c) combination of previous two [1].


Filtering of input image

Filtering [3] an input fingerprint image q is performed as follow:

• The FFT F of input fingerprint image q is computed, here u= 0, 1, 2,…, 31 and v = 0, 1, 2,…, 31.

• Each directional filter Pi is point-by-point multiplied by F, obtaining n filtered image transforms PFi, i = 1, . . . , n.

• Inverse FFT is computed for each PFi resulting in n filtered images p fi, i = 1, . . . , n (spatial domain) [3]. For x = 0, 1, 2 …31 and y = 0, 1, 2 ...31.

• The enhanced image is obtain in following manner: all pixels in one block of enhanced image take the value of pixels on the same position from the filtered image which emphasizes determined orientation for corresponding block [3].


(10)

(11)


Block Diagram of a Fingerprint Enhancement Algorithm


Fig. 9 Block diagram of a fingerprint enhancement algorithm [12].


Butterworth Filter

• The band pass Butterworth filter [3] for radial component Hr(ρ) of order k (usually k = 2), having centre frequency ρ0 and bandwidth ρBW [3] is given as:

and the directional component is given by (13)

• Where ϕBW is the angular bandwidth, and ϕc is the orientation of the filter [3].


(12)

(13)


Frequency response of Butterworth Filter


Fig.10 Butterworth bandpass frequency response


Gabor Filter

• Gabor filters are very useful both in frequency and spatial domain, due to their frequency-selective and orientation-selective properties [4].

• By simple adjustment of mutually independent parameters, Gabor filters can be configured for different shapes, orientations, different width of band pass and different central frequencies [4, 6].

• An even Symmetric Gabor filter general form [4] in the spatial domain [1] is given by


(16)

(14)

(15)


• ϕ is the orientation of the Gabor filter, f is the frequency of the sinusoidal plane wave along thex-axis, σx and σy are the standard deviations of the Gaussian envelope along the x and y axes,respectively.

• The modulation transfer function (MTF) [4] of the Gabor filter can be represented as,

here σu = 1/2πσx and σv = 1/2πσy. The filter is more immune to noise, if σx and σy are significantlylarge, but is more likely to create unauthentic ridges and valleys. The filter is not effective inremoving the noise, if standard deviations are too small


(17)

(20)

(19)

(18)

(21)


An even symmetric Gabor filter and its MTF


Fig. 11 An even-symmetric Gabor filter. (a) The Gabor filter with f = 10 and ϕ = 0. (b) The corresponding MTF [4].


Fingerprint Identification• For fingerprint identification it is ideal to get representations of fingerprints which are invariant

with reference to scale, translation and rotation [22].

• The scale variance difficulty can be eliminated easily since most fingerprint images could be scaled as per the dpi specification of the sensors.

• To remove the other two variance problems a reference frame can be formed which is rotation and translation invariant [22].

• The translation invariance is handled by establishing a single reference point (core point). This reference point is obtained based on the assumption that all the fingerprints are vertically oriented.

• But practically the fingerprint images may be oriented up to ± 45º away from actual assumed vertical orientation [22].

• Cyclic rotation of the feature values in the Fingercode in the matching stage handles this image rotation partially.



Reference Point Location• The reference point or core point of a fingerprint is the point at which the curvature of the

concave ridges is maximum as shown in Fig.12.

• After finding the smoothened orientation image in section 3.2. From (8) compute E, an image containing only the sine component of O′ [22].

• Integrate pixel intensities R1 and R2 for each pixel (i, j) in E as shown in Fig. 13. Assign the value of their difference in corresponding pixels to A (A label image which indicates the reference point is initialized)[22].


(22)

Fig. 12 Concave and convex ridges in a fingerprint image when the finger is positioned upright [22].

(23)


Reference Point Location• On a large database a reference point algorithm is used to empirically determine the regions R1

and R2 [22].

• The maximum curvature in concave ridges can be captured making use of the geometry of regions R1 and R2 [22].

• Find the maximum value in A [22] and assign its coordinate to the core, i.e., the reference point.


Fig. 13 Regions for integrating E pixel intensities for A (i, j) [22].


Feature vector• A fingerprint image can be sectored into a total of 16 × 5 = 80 sectors (S0 through S79) whose core

point [22] is the center of these sectors as shown in Fig. 14.

• Let Fiϕ (x, y) be the ϕ - direction filtered image for sector Si. Now i ϵ {1,2,3,…79} and ϕ ϵ {0º, 22.5º, 45º, 67.5º, 90º, 112.5º, 135º, 157.5º}.

• The feature value Viϕ [22]is the average absolute deviation from mean defined as

• where ni is the number of pixels in Si and Pi ϕ is the mean of pixel values in a sector. The average absolute deviation of each sector in each of the eight filtered images defines the components of the feature vector.


Fig. 14 Reference point (x), the region of interest, and 80 sectors [22].

(24)


Fingerprint matching• The Euclidean distance between the corresponding Fingercodes is used for fingerprint matching

[22].

• A Fingercode is a compact length code obtained by the filter-based bank algorithm in [22] which uses a bank of Gabor filters to capture both local and global details in a fingerprint.

• Reference point removes the translation variance problem [22].

• To eliminate rotational variance the Fingercode is rotated cyclically [22].

• Equations (25), (26) and (27) give single step cyclic rotation [22] of the features of the Fingercode.

• This corresponds to a feature vector which would be obtained if the image were rotated by 22.5º.

• A rotation by R steps corresponds to a rotation R × 22.5º of the image.

• A positive and negative rotation implies clockwise and counterclockwise rotation respectively. TheFingercode [22] obtained after R steps of rotation is given by


(26)

(27)

(25)


Fingerprint matching

• where m is the number of sectors in a band, i ϵ {0,1 , 2,…79} and ϕ ϵ {0º, 22.5º, 45º, 67.5º, 90º, 112.5º, 135º, 157.5º}.

• Five templates are stored corresponding to the following five rotations of the Fingercode: 𝑉𝑖𝜙−2,

𝑉𝑖𝜙−1, 𝑉𝑖𝜙

0 , 𝑉𝑖𝜙1 𝑎𝑛𝑑 𝑉𝑖𝜙

2 [22].

• This Fingercode corresponds to 22.5º rotation. So to make the code more robust we need rotation corresponding to 11.25º [22].

• The original image is rotated by 11.25º and the corresponding five templates are stored. Making a total of ten templates.

• These ten templates give ten Fingercodes.

• So in order to perform matching the Fingercodes of the input image is compared with the Fingercodes in the database [22]. And the Fingercodes with least Euclidian distance is matched.



Implementation

Description of the database:

• Used the database from FVC 2004

• These images are greyscale images of size 640x480 and 96dpi spatial resolution.

• Normalization

• The parameters used for normalization

• M0 =100

• V0 =100


EE5359 Multimedia Processing - Spring 2015 34Project Interim- Fingerprint Enhancement and identification by

Adaptive Directional Filtering

(1)

(b)

(c)(a) (3)

(2)(d) (4)

Fig. 15 a, b, c and d: Original fingerprint images; 1, 2, 3 and 4: Corresponding normalized images.


Ridge orientation and frequency

• Input fingerprint images are divided into non-overlapping blocks of size 8 × 8.

• Then the gradients gx(i, j) and gy(i, j) for each pixel (i, j) of the block, are calculated by Sobel edge-emphasizing filter.

• Average squared gradient and average gradient direction computed from the above values.

• These images are smoothened using a 2D-LPF filter and noise is eliminated.


EE5359 Multimedia Processing - Spring 2015 36Project Interim- Fingerprint Enhancement and identification by

Adaptive Directional Filtering

Fig. 16 (a) and (b) are original fingerprint images; (i) and (ii) are their respective Edge detected images; (1) and (2) are their respective Gradient images.


Orientation images using quiver plots


Fig. 17 (a), (b) and (c) are original fingerprint images; (1), (2) and (3) are their respective orientation image quiver plots.


Directional filtering using Gabor filter bank

• The inter-ridge distance in the fingerprint image is the main factor in determining the parameters Ω, σx and σy, for optimal Gabor filter operation.

• If Ω is too large spurious ridges are created in the filtered image, whereas if Ω is too small nearby ridges are merged into one.

• We set parameters to be Ω = 1/5, and σx = σy = 4.0 [21].

• Eight different directional Gabor filters are used.

• Eight different values for ϕ = iπ/8 (0º, 22.5º, 45º, 67.5º, 90º, 112.5º, 135º, 157.5º) with respect to the x-axis are used.

• A 0º oriented filter accentuates those ridges which are parallel to the x-axis and smoothens the ridges in the other directions [21].



Enhanced fingerprint images- Gabor filter


Fig. 18 (a), (b) and (c) are original fingerprint images; (1), (2) and (3) are the enhanced images obtained by directional filtering using a series of Gabor filters.


Scope of the Project

• The objective of this project is to apply the algorithm proposed in section 3 to smudged and corrupted fingerprints to obtain enhanced images.

• This is done by adaptive directional filtering in the frequency domain by using Butterworth [2] and Gabor filters [1] for fingerprint image enhancement and also for removing noise.

• MATLAB is used to normalize the corrupted fingerprints. Then the frequency and ridge orientation are computed for each fingerprint image.

• After that the image is filtered using directional filters. Here Butterworth and Gabor filters are used to obtain an enhanced image. The quality of the images obtained from both filters is compared visually.

• Fingerprint identification is done using MATLAB coding on the filtered enhanced image by detecting reference point and storing a feature vector in the form of a Fingercode in a data file. This data file is used as a database for fingerprint matching [21].



Future work

• Fingerprint enhancement using Butterworth filter

• Comparing the enhanced images from Gabor and Butterworth filters.

• Fingerprint matching using reference point position and Fingercode.



References• [1] A. M. Raiˇcevi´c and B. M. Popovi´c, “An Effective and Robust Fingerprint Enhancement by Adaptive

Filtering in Frequency Domain”, Facta Universitatis (NIS) Ser.: Elec. Energ., vol. 22, no. 1, pp.91-104, April 2009.

• [2] J. E. Hoover, “The Science of Fingerprints: Classification and Uses”, Federal Bureau of Investigation, Washington, D.C., Aug. 2006.

• [3] B. G. Sherlock, D. M. Monro and K. Millard, “Fingerprint enhancement by directional Fourier filtering,” IEE Proc. Vision Image Signal Process., vol. 141, no. 2, pp.87–94, April 1994.

• [4] L. Hong, Y. Wan and A. K. Jain, “Fingerprint image enhancement: Algorithm and performance evaluation,” IEEE Trans. Pattern Anal. Machine Intell. vol. 20, no. 8, pp. 777–789, Aug. 1998.

• [5] A. Willis and L. Myers, “A cost-effective fingerprint recognition system for use with low-quality prints and damaged fingertips,” Pattern Recognition, vol. 34, pp. 255–270, Jan. 2001.

• [6] J. Yang, L. Lin, T. Jiang and Y. Fan, “A modified Gabor filter design method for fingerprint image enhancement,” Pattern Recognition Letters, vol. 24, pp. 1805–1817, Jan. 2003.

• [7] L. Hong, A.K. Jain, S. Pankanti and R. Bolle, “Fingerprint Enhancement,” Proc. First IEEE WACV, pp. 202-207, Sarasota, Fla., Dec. 1996.



References • [8] T. Kamei and M. Mizoguchi, “Image Filter Design for Fingerprint Enhancement,”

Proc. ISCV’ 95, pp. 109-114, Coral Gables, Fla., Nov. 1995.

• [9] K. Karu and A.K. Jain, “Fingerprint Classification,” Pattern Recognition, vol. 29, no. 3, pp. 389-404, July 1996.

• [10] L. O’Gorman and J.V. Nickerson, “An Approach to Fingerprint Filter Design,” Pattern Recognition, vol. 22, no. 1, pp. 29-38, Jan. 1989.

• [11] N. Ratha, S. Chen and A.K. Jain, “Adaptive Flow Orientation Based Feature Extraction in Fingerprint Images,” Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, March 1995.

• [12] Y. Wang, J. Hu and F. Han, “Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields”, Applied Mathematics and Computation, vol. 185, no. 2, pp. 823-833, Feb. 2007.

• [13] D. L. Hartmann, “Filtering of Time Series”, 2014. [Online]. Available: http://www.atmos.washington.edu/~dennis/552_Notes_7.pdf

• [14] S. Chikkerur, A. N. Cartwright and V. Govindaraju, “Fingerprint enhancement using STFT analysis”, Pattern Recognition, vol. 40, pp. 198-211, Jan. 2007.

• [15] R. Iwai and H. Yoshimura, "A New Method for Improving Robustness of Registered Fingerprint Data Using the Fractional Fourier Transform", International Journal of Communications, Network and System Sciences, vol. 3, no. 9, pp. 722-729, Sept. 2010.



References • [16] A. Sherstinsky and R.W. Picard, “Restoration and Enhancement of Fingerprint Images Using M-Lattice: A

Novel Non-Linear Dynamical System,” Proc. 12th ICPR-B, pp. 195–200, Oct. 1994.

• [17] E. Bezhani, D. Sun, J. Nagel and S. Carrato, “Optimized filterbank fingerprint recognition”, Proc. SPIE 5014, Image Processing: Algorithms and Systems, vol. 2, pp. 20-24, May 2003.

• [18] Project Idea, EE5359-Multimedia Processing Course Website. [Online]. Available:http://www.uta.edu/faculty/krrao/dip/Courses/EE5359/index_tem.html

• [19] P. Salil, J. Anil and P. Sharath, “Learning fingerprint minutiae location and type,” Pattern Recognition, vol. 36, pp. 1847-1857, Oct. 2003.

• [20] MATLAB version 8.1.0.604, Release R2013a, The MathWorks, Inc., Natick, Massachusetts, United States, Feb 2013.

• [21] E. Zhu, J. Yin, G. Zhang and C. Hu, “A Gabor Filter Based Fingerprint Enhancement Scheme Using average Frequency,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 20, no. 3, pp. 417-429, May 2006.

• [22] A. K. Jain, S. Prabhakar, L. Hong and S. Pankanti,“Filterbank-Based Fingerprint Matching”, IEEE Transactions on Image Processing, vol. 9, no. 5, pp.846-859, May 2000.

• [23] A. K. Jain, S. Prabhakar and L. Hong, “A multichannel approach to fingerprint classification,” IEEE Trans. Pattern Anal. Machine Intell. vol. 21, no. 4, pp. 348–359, Apr. 1999.



References • [24] Fingerprint Verification Competition, The Biometric system lab, University of Bologna, Cesena-Italy, 2004.

[Online]. Available: http://bias.csr.unibo.it/fvc2004/download.asp.

• [25] FBI Fingerprint Database, Washington, D. C., United States. [Online]. Available: http://www.fbi.gov/about-us/cjis/fingerprints_biometrics/iafis/iafis.

• [26] K. R. Rao and S. Chakraborthy, “Fingerprint Enhancement by Directional Filtering”, ECTI-CON, Hua Hin, Thailand, 16-18 May 2012.

• [27] K. R. Rao, D. N. Kim and J. J. Hwang, “Fast Fourier Transform - Algorithms and Applications”, Springer Science & Business Media, New York, 2010.

• [28] S. Chikkerur, C. Wu and V. Govindaraju, "A systematic approach for feature extraction in fingerprint images", ICBA, LCNS, vol. 3072, pp. 344-350, 15-17 July 2004.

• [29] K. Nilsson and J. Bigun, “Localization of corresponding points in fingerprints by complex filtering”, Pattern Recognition Letters, vol. 24, no. 13, pp. 2135-2144, Sept. 2003.

• [30] A. R. Rao, “A Taxonomy for Texture Description and Identification”, Springer Series in Perception Engineering, New York, 2012.

• [31] M. Kass and A. Witkin. "Analyzing oriented patterns", Computer vision, graphics and image processing, vol. 37, no. 3, pp. 362-385, March 1987.

• [32] Image Processing Toolbox User’s guide, The MathWorks, Inc., Natick, Massachusetts, United States, March 2015. [Online]. Available: http://nl.mathworks.com/help/pdf_doc/images/images_tb.pdf.

• [33] L. Rosa, MATLAB Fingerprint Recognition Functions Code, L'Aquila, Italy.[Online]. Available: http://www.advancedsourcecode.com/fingerprint.asp.


http://www.fbi.gov/about-us/cjis/fingerprints_biometrics/iafis/iafis

http://nl.mathworks.com/help/pdf_doc/images/images_tb.pdf

http://www.advancedsourcecode.com/fingerprint.asp

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