a systematic approach for feature extraction in fingerprint images

A Systematic Approach For Feature Extraction in Fingerprint

Images

Sharat Chikkerur, Chaohang Wu, Venu Govindaraju{ssc5,cwu3,govind}@buffalo.edu

Abstract

A new enhancement algorithm based on Fourier domain analysis is proposed.

Fourier analysis is used extract orientation, frequency and quality map in addition to doing enhancement.

The enhancement algorithm uses full contextual information and adapts radial and angular extents based on block properties.

A new feature extraction algorithm based on chain code analysis is presented.

An objective metric is used to evaluate the efficiency of the feature extraction.

Outline

Related Previous Work Overview of the proposed method Fourier Analysis Fingerprint Image Enhancement Feature Extraction Performance Evaluation Conclusion

Motivation : Enhancement Anisotropic filter (Greenberg et.al, Yang et.al)

Very fast but cannot handle creases, wide breaks and poor quality images

Pseudo Matched filtering (Wilson, Grother Candela et. al) Increases SNR but can lead to artefacts due to isotropic filtering.

Directional Filtering (Sherlock,Monro et. al.) Very robust even near regions of high curvature but marked by large storage

requirements. Frequency of ridges is assumed to be constant.

Gabor filter bank(Hong et. al) Filter has optimal joint directional and frequency resolution but does not handle

high curvature regions well due to block wise approach. Angular and radial bandwidths are constant.

Proposed approach A single algorithm is used for contextual analysis and enhancement. Utilized full contextual information. Adapts both frequency and angular

bandwidth based on block properties. Adapts to high curvature regions reducing blocking artifacts. However, using full contextual information leads to processing complexity.

Qualitative Comparison : Feature Extraction

MINDTCT,NIST NFIS, (Garris et. al) The algorithm is extremely fast. Greedy approach to minutia detection leads to false positives.Extensive post

processing is required to eliminate false positives Adaptive Flow Orientation technique (Ratha et. al.)

Is capable of correcting breaks in the rides and is robust to noise. Peak detection leads to false positivies in regions of poor ridge constrast.Also,

thinning and morphological post processing shift minutia location. Direct Gray Scale Ridge Following (Maio and Maltoni)

Does not have errors introduced due to binarization and has low computational complexity.

Cannot handle poor contrast prints and images with poor ridge structure. Proposed method

Enhancement reduces spurious and missing minutiae. The locations of the minutiae are preserved during detection.

Contour based extraction is sensitive to binarization and enhancement errors.

Outline


Overview of the proposed method

Fourier AnalysisContextual

FilteringPreprocessing

BinarizationContour ExtractionMinutiae Detection

Enhancement

Feature ExtractionFeature Extraction

Gray LevelImage





Enhancement


Gray LevelImage

SNR is increased using Pseudo Matched filtering[Wilson et. Al, 1994], k = 0.15 is used to reduce artifacts





Enhancement

Gray LevelImage

The image is divided into blocks and Fourier analysis is done on each of them. The analysis produces orientation, frequency, angular bandwidth and quality maps [proposed]





Enhancement


Gray LevelImage

Each block is filtered using a orientation and frequency selective filter [Sherlock and Monro, 1994] with the given bandwidth





Enhancement


Gray LevelImage

The enhanced image is binarized using an locallyadaptive algorithm





Enhancement


Gray LevelImage

Contours of the ridges are extracted and traced consistently in a counter clockwise direction[Govindaraju et. al, 2003]





Enhancement


Gray LevelImage

Minutiae are detected as points with'signficant' turns in the contour. Vector products are used to quanity the turns

Outline


Surface Wave Model

),(

),(

)sin()cos(2cos),(

yx

yx

r

yxrAyxi

Local ridge orientation

Local ridge frequency

Validity of the model

With the exception of singularities such as core and delta, any local region of the fingerprint has consistent ridge orientation and frequency.

The ridge flow may be coarsely approximated using an oriented surface wave that can be identified using a single frequency f and orientation .

However, a real fingerprint is marked by a distribution of multiple frequencies and orientation.

Obtaining block parameters

To obtain the dominant ridge orientation and frequency a probabilistic approximation is used

We can represent the Fourier spectrum in polar form as F(r, ) The power spectrum is reduced to a joint probability density function using

The angular and frequency densities are given by marginal density functions

r

drdrF

rFrp

2

2

),(

),(),(

,

r

drrfp ),()(

drfrp ),()(

Obtaining block parameters (contd.)

The dominant ridge orientation is obtained using

The dominant frequency can be estimated using the expected value of the frequency density function,

The quality is assumed to be proportional to the strength of the ridge flow and is estimated using

dp

dp

)()2cos(

)()2sin(

tan2

1}{ 1

r

drrprr )(}{

drdrFE

2),(log

Fourier Analysis –Energy Map

Original Image Energy Map

2

),(logu v

vuFE

Original Image Local Ridge Frequency Map

Fourier Analysis – Frequency Map

r

r rF

rF

rprprrE

2

2

),(

),(

)(,)(.}{

Original Image Local Ridge Orientation Map

Fourier Analysis-Orientation Map

r

r

rF

rFp

p

p

E

2

2

1

),(

),()(,

)()2sin(

)()2cos(

tan2

1}{

Fourier Analysis : Angular Bandwidth

ondistributigaussian assuming ,2

)(

p

BWBW

Outline


Original Image

Fourier Domain Based Enhancement

1994] al, et.[Sherlock

frequencymean :}{r n,orientatiomean :}{

bandwidth,angular :,bandwidth radial:

otherwise 0

if 2

)(cos

)( )()(

)()(

)()(),(

c

2

2222

2

rEE

r

Hrrrr

rrrH

HrHrH

c

BWBW

BWcBW

C

nc

nBW

nBW

r

r

Enhanced ImageContextual Filter

Additional Enhancement Results

Outline


When the ridge contours are traced in a counter clockwise direction, minutiae are encountered as points with significant turn.

Types of turn points: left(ridge),right(bifurcation)

S(Pin, Pout) = S( )=S(x1y2 –x2y1)

Pin : Vector leading into the candidate point

Pout: Vector leading out of the point of interest

S(Pin, Pout) >0 indicates left turn, S(Pin, Pout) <0 indicates right

turn

Significant turn can be determined by

( )=x1y1 + x2y2 < T

Determination of Turn Points

outin P x P

outin P . P

Turn points

(a) Potential minutia location; (b) Determination of turn points

Post processing

• Feature Extraction errors• Missing minutiae • Spurious minutiae

• Spurious minutia can be removed using post processing• Heuristic rules: 1. Merge minutiae that are a certain distance of each other and have similar angles2. Discard minutiae whose angles are inconsistent with ridge direction3. Discard all border minutia4. Discard opposing minutiae within certain distance of each other

Example Result

Outline


Quantitative Analysis

Test Data 150 prints from FVC2002(DB1) were randomly selected for

evaluation. Ground truth was established using a semi automated truthing tool. Results compared using NIST NFIS open source software.

Metrics We use feature extraction metrics proposed by Sherlock et. Al Sensitivity: Ability of the algorithm to detect true minutiae Specificity : Ability of the algorithm to avoid false positives Additional Metrics

Flipped : Minutiae whose type has been exchanged

Truth Ground:N Exchanged, :E positives, False :FP Negatives, False :FN

,1,1N

EFlipped

N

FPySpecificit

N

FNySensitivit

Quantitative Analysis : Results

Examples

File Name NIST Proposed method

Actual TP FP M F TP FP M F

10_8.tif 18 16 8 2 1 17 0 1 1

11_6.tif 50 40 4 10 2 41 4 9 4

12_8.tif 29 22 5 7 3 22 3 7 1

13_6.tif 35 28 10 7 4 28 10 7 2

14_6.tif 44 34 12 10 6 37 13 7 5

15_7.tif 38 37 7 1 5 37 3 1 0

16_7.tif 41 35 12 6 5 36 8 5 8

17_6.tif 43 35 16 8 11 36 7 8 11

18_8.tif 34 31 7 3 4 32 6 2 1

19_7.tif 35 26 8 9 3 31 6 4 5

Quantitative Analysis : Results

Summary results Count TP(ANSI) > proposed : 40 of 150 Count E(ANSI) < proposed : 40 of 150

Metric NIST Proposed

Sensitivity(%) 82.8 83.5

Specificity(%) 77.2 76.8

Flipped(%) 12.0 10.9

Sensitivity distribution Overall statistics

Conclusion

A new effective enhancement algorithm based on

Fourier domain analysis is proposed

A single algorithm is used to derive orientation,

frequency, angular bandwidth and quality maps

A new feature extraction algorithm based on

chain code contour analysis is presented

Heuristic rules specific to the feature extraction

algorithm has been derived

The algorithm is evaluated using an objective

metric

Thank You

http://www.cubs.buffalo.edu

Related Previous Work: Enhancement Spatial Domain

Anisotropic filter (Greenberg et.al, Yang et.al) Uses a locally adaptive kernel Blurs along the ridge direction. Increases the discrimination between ridges

and valleys along the perpendicular direction. Frequency Domain

Pseudo Matched filtering (Wilson, Grother Candela et. al) The Fourier transform of the block is multiplied by its power spectrum raised

to a power of k

Directional Filtering (Sherlock,Monro et. al.) The image is decomposed into a set of eight directional responses using a

bank of directionally selective filters. The frequency is assumed constant. The enhanced image is obtained by composing the filter responses using the

local orientations.

Gabor filter bank(Hong et. al) The image is enhanced by using a Gabor filter bank Gabor fillters have the optimum orientation and frequency resolution.

kvuFvuFFyxi ),(),(),( 1enh

Related Previous Work: Feature Extraction Binarized Images

MINDTCT, NIST NFIS, (Garris et. al) An oriented grid is placed at each pixel and the projection sums are taken at

each row. The pixel is assigned 0 if the projections sum at the center row is less than average, otherwise the pixel is assigned 1

The minutiae are detected using structural rules.

Adaptive Flow Orientation technique (Ratha et. al.) Orientation of each 16x16 block is determined by computing the gray level

projections at various angles. The projection along a scan line perpendicular the ridge direction has maximum variance.

The image is binarized by detecting the peaks along this scan line. The minutiae are detected using the thinned image

Gray Scale Image Direct Gray Scale Ridge Following (Maio and Maltoni)

A set of starting points are chosen by superimposing a grid on the image The ridge is traced from each starting point until a bifurcation or ridge

ending is found. A labelling strategy is used to preven traversing the same ridge twice.

a systematic approach for feature extraction in fingerprint images

Documents

proposed methodenhancement

new enhancement algorithm

contextual analysis

regions of poor ridge

yang et

garris et

monro et

enhancement errors