paper presentation 3

By : Main Mahmood Ali

SEQUENCEBIOMETRICSPALMPRINTIDENTIFICATION METHODSWHY LINE BASED APPROACHESORIENTED HAUSDORFF SIMILARITY

MEASUREFLOW DIAGRAM OF PALMPRINT

RECOGNITION SYSTEMPREPROCESSINGFEATURE EXTRACTIONMATCHINGACHIEVEMENTS

INTRODUCTIONPublic security issue

Controlled access to Personal information and data.Controlled access to work place. Like some R&D area.Prevention of cross border false infiltration.

Conventional solutions for Security implementationLock and keysPasswords and PIN codes, (Bank Account numbers, PC

passwords)Access cards, (passports, CNIC, ATM, Driving license

etc.)

Drawbacks of Traditional methodsProne to Theft/Hacking, Cloning, Forging, Forgetting Multiple passwords for different accesses for same

person.Required to be changed frequently

BIOMETRICSBiometrics is the study or a science involving

automated identification of human based on physical or behavioral characteristics.

PhysiologicalCharacteristics

Ear

Face

DNA

Finger

Iris

Palm print

TYPES OF BIOMETRICS [1]

Behavioral characteristics

Voice Pattern

Typing strokes’ Pattern

Signature

Walk or GAIT Pattern

DRAWBACKS [1]FINGER PRINT

Less area of interestHigh resolution images Effects of labor and aging

IRISUser acceptance Highly expensive hardware

FACE RECOGNITIONComplex computational situationsIllumination and OcclusionLow accuracy rate

VOICEAging and illness effectEffected by hardware / ambientnoiseLow accuracy rates

PALMPRINT [1]Larger area of interestAdditional information as

compared to fingersMore stable features than

fingersCheap Hardware than Iris

capturing devicesLow resolution imagesGreater user acceptance

PALMPRINT STRUCTURE[8]On line and off line imagesHigh and low resolution

imagesPeg based

DATABASEData base features [20]

7752 palm images 386 individuals20 samples per personTwo sub-data bases

IDENTIFICATION METHODSSub-space approaches

Feature : Wavelets, DCT, Gabor filtering etc.Subspace: PCA, LDA, ICAClassifier: Neural Networks, cosine distance,

Euclidean distanceStatistical approaches

Feature : Wavelets, Sobel filter, morphological operators etc.

Subspace: Mean and Standard deviation, Mean EnergyClassifier: Neural Networks, cosine similarity,

Euclidean distance

SCOPE OF RESEARCHEdge based palmprint recognition using

Hausdorff similarity measure

Line based approaches have additional advantages like Low resolution images are required as edges are

utilized for basic features Computationally less expensive and Better results

in illumination variation Classifier is usually a distance transform Low cost imaging devices

Classical Hausdorff distance . Gyu-Sin et al [26].

HAUSDORFF DISTANCE TRANSFORM

A B,h,B A,hmaxB A,H

baBbAa minmaxB A,h

ORIENTED HAUSDORFF SIMILARITY (OHS) . [23]

)()()(OHS .B A,H aB

Aa

aBaA ddAdA

)(OHS B A,H aBAa

daS

functionLimitingd

apositionatAimageofvectorgradientunitdA

danddAbetweenproductDotaS

aB

aA

aBaA

)(

)(

)()(

)(

FLOW DIAGRAM OF PALMPRINT RECOGNITION SYSTEM

Boundary Detection

Distance transform

Center detection

ROI extraction

ROI SEGMENTATIONROI

EXTRACTION

EDGE DETECTION

DT MAP

EDGE ORIENTATION

MATCHING

Input image

EDGE IMAGE

Roberts operators

Prewitt

Sobel

Canny[21]

CANNY EDGEProvided the edges of the

palm linesProvided the edge orientationsCatered the illumination

problem

ROI EXTRACTION

EDGE DETECTION &

EDGE ORIENTATION

DT MAP

MATCHING

DISTANCE TRANSFORM[22]Euclidian distance

Chessboard

City block/ Taxicab

221

221 )()(d yyxx

)y2– y1,– x2 x1max(d

)y2– y1– x2 x1(d

PRE PROCESSING

AND ROI

EXTRACTION

EDGE DETECTION

DT MAP

EDGE ORIENTATION

MATCHING

DISTANCE TRANSFORM

PRE PROCESSING

AND ROI

EXTRACTION

EDGE DETECTION

DT MAP

EDGE ORIENTATION

MATCHING

Distance Transform

PRE PROCESSING

AND ROI

EXTRACTION

EDGE DETECTION

DT MAP

EDGE ORIENTATION

MATCHING

MATCHING SEQUENCE ROIs of test and reference images are

extracted from input imageEdge images of test and reference images

are computed.Oriented Hausdorff similarity is calculatedThe maximum similarity is the classifier of

matchingEach 81 templates test image is matched

with of the reference image. Maximum similarity template is the

matched template

PRE PROCESSING

AND ROI

EXTRACTION

EDGE DETECTION

DT MAP

EDGE ORIENTATION

MATCHING

PALM 1 PALM 2

128x128 cropped image

128x128 cropped image

Edge Image

Distance map

INTRA CLASS LINEAR TRANSLATION

imagesROI

EXTRACTION

EDGE DETECTION

DT MAP

EDGE ORIENTATION

MATCHING

SLAVE CENTER POINTS : Linear Translation Compensation

Total of 74305 matches for imposters and 13896 for Genuine were made for recognition.

After linear translation compensation results are improved by 13%.

An EER of 2.76% has been achieved.

ACHIEVEMENTSPRE

PROCESSINGAND ROI

EXTRACTION

EDGE DETECTION

DT MAP

EDGE ORIENTATION

MATCHING

COMPARISON 

Duta et al.

Kumar et al

Zhang & Zhang

Masood et al

Proposed

Algorithm

    [11] [10] [12] [4]  

Databas

e

Subject

3 100 50 50 386

Images

30   200 500 3474

FeatureFeature points

Texture and

feature points

Wavelet signature

Texture Lines

Matching criteria

Point Pattern Matching using Euclidian distance

Euclidian distance

Euclidian distance

Wavelet (Wavelet combinat

ion)

ROHS

EER (%) 5 3 3 4.07 2.76

Q & A

REFERENCES

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[20] PolyU Palmprint database, presently available at:http://www.comp.poly.edu.hk/biometrics/

[21] J. F. Canny. “A Computational Approach to Edge Detection”. IEEE. Transaction. Pattern Analysis and Machine Intelligence, 1986, pp. 679-698,.

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