paper presentation 3
DESCRIPTION
COMSATS Institute of Information Technology Abbottabad FIT 2011TRANSCRIPT
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
[1] Anil K. Jain, Patrick Flynn, Arun A. Ross, “Handbook of Biometrics,” ISBN: 978-0-387-71040-2, 2008. [2] T. Connie, A.T.B. Jin, M.G.K. Ong, D.N.C. Ling, An automated palmprint
recognition system, Image and Vision Computing 23 (5) (2005) 501–515. [3] X.Y. Jing, D. Zhang, A face and palmprint recognition approach based on
discriminant DCT feature extraction, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 34 (6) (2004) 2405–2415.
[4]G.M. Lu, K.Q. Wang, D. Zhang, Wavelet based independent component analysis for palmprint identification, in: Proceedings of International Conference on Machine Learning and Cybernetics, vol. 6, 2004, pp. 3547–3550.
[5] G. Feng, K. Dong, D. Hu, D. Zhang, When face are combined with palmprints: a novel biometric fusion strategy, in: Lecture Notes in Computer Science, Springer, vol. 3072, 2004, pp. 701–707.
[6] R. Chu, Z. Lei, Y. Han, S.Z. Li, Learning Gabor magnitude features for palmprint recognition, ACCV, 2007, pp. 22–31.
[7] M. Ekinci, M. Aykut, Palmprint recognition by applying wavelet subband representation and kernel PCA, Lecture Notes in Artificial Intelligence, 2007, pp. 628–642.
[8] Adams Kong, David Zhang, Mohamed Kamel, “A survey of palmprint recognition”, Pattern Recognition, Volume 43, Issue 7, 2009, pp. 1408-1418.
[9] C.C. Han, H.L. Cheng, C.L. Lin, K.C. Fan, Personal authentication using palm-print features, Pattern Recognition 36 (2) (2003) 371–381.
[10] G. Lu, K. Wang, D. Zhang, Wavelet based feature extraction for palmprint identification, in: Proceeding of Second International Conference on Image and Graphics, 2002, pp. 780–784.
[11] A. Kumar, H.C. Shen, Palmprint identification using PalmCodes, in: Proceedings of 3rd International Conference on Image and Graphics, 2004, pp. 258–261.
[12] C. Poon, D.C.M. Wong, H.C. Shen, Personal identification and verification: fusion of palmprint representations, in: Proceedings of International Conference on Biometric Authentication, 2004, pp. 782–788.
[13] J.S. Noh, K.H. Rhee, Palmprint identification algorithm using Hu invariant moments and Otsu binarization, in: Proceeding of Fourth Annual ACIS International Conference on Computer and Information Science, 2005, pp. 94–99.
[14] L. Zhang, D. Zhang, Characterization of palmprints by wavelet signatures via directional context modeling, IEEE Transactions on Systems, Man and Cybernetics, Part B 34 (3) (2004) 1335–1347.
[15] X.Wu, K.Wang, D.Zhang, “Line Feature Extraction and Matching in Palmprint”, in: Proceeding of the Second International Conference on Image and Graphics, 2002, pp.583–590.
[16] Laura Liu and David Zhang “Palm-Line Detection”, International Conference on Image Processing (ICIP) : Genova, Italy, v. 3, 2005, pp. 269-272.
[17] Fang Li, Maylor K.H. Leung, Xiaozhou Yu,” Palmprint Matching Using Line Features” The 8th International Conference ,Advanced Communication Technology, ICACT 2006.
[18] David Zhang, Wai-Kin Kong, Jane You, and Michael Wong, “Online Palmprint Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, September 2003.pp1041-1050.
[19]. Fang Li, Maylor K.H. Leung, “Two-stage Approach for Palm print Identification” ICARCV 9th International Conference on Control, Automation, Robotics and Vision, December 2006.
[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,.
[22] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins. “Digital Image Processing Using MATLAB”, Second Edition 2007, printed by Dorling Kindersley (India) Pvt. Ltd. Under license from Pearson Education, Inc.
[23] Dong-Gyu-Sin, Rae-Hong Park “Oriented Hausdorff Similarity Measure of object matching”. MVA ’98 IAPR workshop on Machine Vision Applications, Makuhari, Chiba, Japan, 1998.
[24] Ajay Kumar, David C. M. Wong, Helen C. Shen, and Anil K. Jain, “Personal Verification Using Palmprint and Hand Geometry Biometric, Proc. 4th Intl. Conf. Audio- and Video-Based Biometric Authentication (AVBPA), Guildford, UK, 2003 , pp. 668-675.
[25] Zhang, and D. Zhang, “Characterization Of Palmprint By Wavelet Signatures Via Directional Context Modeling”, IEEE TSMC(B), 34(3), 2004. pp.1335-1347
[26] H Masood, M Mumtaz, M A Afzal Butt, AB Mansoor and S A Khan, “ Wavelet Based Palmprint Authentication System”. Proc. of IEEE. International Symposium of Biometric and security Technologies, Islamabad, Pakistan, 2008.
[27] M A Asif and H Masood ,“Palmprint Identification Using Contourlet Transform” 2nd IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS. 2008.pp.1-5.