fingerprining

1

Fingerprint Recognition

Abhishek Verma (99050056)

2

Fingerprint Recognition

Outline:Introduction

My Project ScopeFingerprint Research Background

Algorithm Overview of My ApproachDetailed DesignConclusion

3

Fingerprint Recognition Introduction

Objective:Study History, MethodologyCompare reported algorithmsImplement a FR systemGive experimental results

Some papers used:•Direct Gray-Scale Minutiae Detection In Fingerprint•Intelligent biometric techniques in fingerprint face recognition•Adaptive flow orientation based feature extraction in fingerprint images•Fingerprint Image Enhancement:Algorithm and Performance Evaluation•Online Fingerprint Verification

4

Introduction-Giving thumbprints thumbs-down

“A judge has ruled that fingerprint evidence is scientifically unreliable “

Economist, 19/Jan/2002

5

IntroductionGiving thumbprints thumbs-up

• Research Paper Statistics

Documents about 'Fingerprint'

0

50

100

150

200

1996 1997 1998 1999 2000 2001 1~4/02

SCI

IEEE

Other Types(Phd thesis,Chinese Periodicals)GB BIG5 Other Database

142 66 54

6

IntroductionGiving thumbprints thumbs-up

• Intensive researches show Fingerprints are scientificallyUniquePermanentUniversal

• The judge just proved:fingerprint recognition is scientifically

difficult

7

Minutiae-Based Approach

Minutiae terminations bifurcations

Ridge Valley

8

Verification (AFAS) vs. Identification (AFIS)

Sensor

MinutiaExtractor

MinutiaeMatcher

SystemDatabase

System Level Design

SystemDatabase

User’s Magnetic Card….

User

1:m MatchIdentification

1:1 MatchVerification

User ID

9

Algorithm Level Design

•Thinning•Minutiae Marking

•Remove False Minutiae

Minutia extraction

Preprocessing•Image Segmentation•Image Enhancement•Image Binarization

Post-processing

Minutia Extractor:

10

Algorithm Level Design

•Find Reference Minutia Pair•Affined Transform•Return Match Score

Minutia Matcher:

11

Minutia Extractor- Segmentation

Block directional estimationForeground : have a dominant directionBackground : No global direction

12

Fingerprint Image Segmentation

Ridge Flow Orientation Estimate Edge detector: get gradient x (gx),gradient y (gy)

Estimate the ß according to:tg2ß = 2 sigma(gx*gy)/sigma(gx

2-gy2)

Region of Interest Morphological Method

Close + Open

13


14


Area Close Open

ROI + Bound

15

Fingerprint Image Enhancement

Histogram Equalization

16

Fingerprint Image Enhancement

Fourier Transform

17

Preprocessing - Enhancement

18

Fingerprint Image Binarization

19

Common Approaches: Local Adaptation

gray value of each pixel g if g > Mean(block gray value) , set g = 1;

Otherwise g = 0

Directly ridge Retrieval from Gray Imageget Ridge Maximums Implying binarization


20


Directly ridge Retrieval1.Estimate ridge direction D 2.Advance by a step length 3.Along the direction orthogonal to D Return to ridge Center 4.go to 11.Block ridge flow orientation O 2.Get direction P orthogonal to O 3.Project block image to the lines along P

21

Minutia extraction stage - Thinning

22

Minutia extraction stage - ThinningMorphological Approaches:

bwmorph(binaryImage,''thin'',Inf)

Parallel thinning algorithm: 1) 2=< N(p1) <= 6 T(p1) = 1 p2 * p4 * p6 = 0 p4 * p6 * p8 = 0 2) 2=< N(p1) <= 6 T(p1) = 1 p2 * p4 * p8 = 0 p2 * p6 * p8 = 0N(p) sum of NeighborsT(p) Transition sum from 0 to 1 and 1 to 0

P9 P2 P3P8 P1 P4P7 P6 P5

23

Preprocessing Steps:0 1 00 1 01 0 1

0 0 00 1 00 0 1

Bifurcation

Termination

Minutia extraction

24

Minutia extraction

25

Post-processing stage

False Minutia Remove:

Two disconnected terminations short distance Same/opposite direction flow

Two terminations at a ridgeare too close

26

Post-processing stage

False Minutia Remove:

27

Minutia Match

Minutia Representation: Mn ( Position, Direction ß, Associate Ridge)

tgß = (yp-y0)/(xp-x0);Xp = sigma(xi)/Lpath;Yp = sigma(yi)/Lpath;

ridge

Minutiax0 x1 x2 x3 x4 x5 x6 x

y

Lpath

Generally, ridge endings and bifurcations are consolidated

28

Simple Relax Match Algorithm :

Minutia Match

1. For each pair of Minutia2. Construct the Transform Matrix

TM = cos

sin

0

sin

cos

0

0

0

1

x

y

xi_new

yi_new

i_new

xi x( )

yi y( )

i

=TM *

(x,y, )(xi,yi, i)

29

Simple Relax Match Algorithm :

Minutia Match

For any two minutia from different image,If They are in a box with small lengthAnd their direction has large consistence

They are Matched Minutia

Match Score = Num(Matched Minutia)

Max(Num Of Minutia (image1,image2));

30

Alignment – based Algorithm :

Minutia Match

ridge

Minutiax0 x1 x2 x3 x4 x5 x6 x

y

Ridge information is used to determine the goodness of areference Minutia pair

Ridge_direction

If two ridge are matched wellContinue use the Relax Box Match OrUse String Match

31

Fingerprint Verification

Performance Evaluation IndexFRR: False Rejection RateFRR = 2/total1

FAR: False Acceptance RateFAR = 3/total2

Total1 = m*(n+1)*n/2Total2 = m*(m-1)/2

Same Finger

Programresult (Yes/No)

DifferentFinger

1 Yes 2 No

3 Yes 4 No

F10 F11 F12 F13 …F1nF20 F21 F22 F23 …F2nF30 F31 F32 F33 …F3nFm0 Fm1 Fm2 Fm3 …Fmn

32

Fingerprint Verification

Thanks

Question and Answer

33

Fingerprint Classification

Left Loop Right Loop

Whorl Arch Tented Arch

DeltaPore

34

IntroductionBiometric Research

FingerprintUnique,Portable,Large storage per finger templateLargest Market SharingFeature: Minutiae & Classification

Face & HandNon-unique,Large operation device,FastFeature: Shape,Area…

Iris & RetinaUnique,Large Device,Less User Safety ConsiderationFeature: Shape,Vein…

35

IntroductionFingerprint Research Topics

Fingerprint Verification & IdentificationMinutiae-Based-ApproachSimilar System & Algorithm Designs

Fingerprint ClassificationFive Categories By Core & Delta Types

Fingerprint image CompressionWSQ Standard

36

Fingerprint ImageCompression

FBI Standard64-sub band structure WSQ

Correlation-Based Approach For Fingerprint Verification

Also called Image-based approach Relatively little work has been conducted Gabor filter; Wavelet Domain Feature Extraction