Master Thesis - Department of Computer Science

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[127] Vladimir N. Vapnik. The Nature of Statistical Learning Theory. Springer Verlog, Heidelberg, DE, 1995. [128] Y. Wang, T. Tan, and A. K. Jain. Combining Face and Iris Biometrics for Identity Verification. In Proc. of Int. Conf. on Audio and Video based Biometric Person Authentication, pages 805–813, Guildford, June 2003. [129] D. M. Weber. A Cost Effective Fingerprint Verification Algorithm for Com- mercial Applications. In South African Symp. on Communication and Signal Processing, 1992. [130] J. Weng, N. Ahuja, and T. S. Huang. Learning Recognition Segmentation of 3-D Objects from 2-D Images. In Int. Workshop Face Gesture Recognition, Zurich, Switzerland, 1995. [131] Laurenz Wiskott, Jean-Marc Fellous, Norbert Kruger, and Christoph von der Malsburg. Face Recognition by Elastic Bunch Graph Matching. IEEE Tran. on Pattern Analysis and Machine Intelligence, 19(7):775–779, July 1997. [132] K. Woods, K. Bowyer, and W. P. Kegelmeyer. Combination of Multiple Clas- sifiers using Local Accuracy Estimates. IEEE Tran. on Pattern Analysis and Machine Intelligence, 19(4):405–410, April 1997. [133] J. D. Woodward, C. Horn, J. Gatune, and A. Thomas. Biometrics : A look at Facial Recognition. RAND Documented Briefing, Tech. Rep., 2003. [134] Huilin Xiong, M. N. S. Swamy, and M. O. Ahmad. Two-Dimensional FLD for Face Recognition. Pattern Recognition, 38:1121–1124, 2005. [135] L. Xu, A. Krzyzak, and C. Y. Suen. Methods for Combining Multiple Classifiers and their Applications to Handwriting Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 22(3):418–435, 1992. [136] M. H. Yang. Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition using Kernel Methods. In IEEE International Conference on Face and Gesture Recognition, pages 215–220, Washington, May 2002. [137] Y. Yoshitomi, T. Miyaura, S. Tomito, and S. Kimura. Face Identification using Thermal Image Processing. In IEEE International Workshop on Robot 154
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DEVELOPMENT OF EFFICIENT METHODS FO
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To My Parents, Sisters and Dearest
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Mirnalinee, Shreyasee, Lalit, Manis
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LDC Linear Discriminant Classifier
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3 An Efficient Method of Face Recog
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A.6 Minutiae Matching . . . . . . .
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4.1 Effect of increasing number of
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List of Figures 2.1 Summary of appr
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3.7 Area difference between genuine
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Abstract Biometrics is a rapidly ev
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CHAPTER 1 Introduction The issues a
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1.1.1 Applications Biometrics has b
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• Spoof Attacks: An impostor may
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• A new approach for multimodal b
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CHAPTER 2 Literature Review This re
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ange [23], infrared scanned [137] a
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u 2 x 2 u 1 x 1 (a) PCA basis (b) P
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PCA Projection Class 1 Class 2 LDA
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F DIFS DFFS F (a) (b) F 1 L Figure
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image A of m rows and n columns is
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faces of 40 subjects. • Others: A
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malizing the vector components, the
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Figure 2.5: A fingerprint image wit
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features (gradient coherence, inten
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Figure 2.7: Examples of minutiae; A
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according to the local orientation
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• Parallel Mode: This operational
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can address the problem of noisy se
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Prior to Matching Sensor Level Feat
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determined by logistic regression.
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Class-indifferent Methods • Decis
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3. The final DS soft vector is calc
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on three face databases and compare
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for a face. This was illustrated by
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Image Rows h(.) g(.) 2 2 Columns Fi
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in the accuracy of face recognition
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(a) (a1) (a2) (a3) (b) (b1) (b2) (b
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Genuine Impostor Scores Scores 1 0
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Error d1 = (1 − TZeroF RR) d2 = |
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The algorithm for obtaining the sub
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each subject, 42 samples (flashes f
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Table 3.3: Peak Recognition Accurac
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Table 3.5: Peak Recognition Accurac
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Table 3.7: Performance of Ekenel’
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Table 3.9: Performance of Ekenel’
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to our proposed method. Section 4.2
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plored both of the spaces to captur
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project only the class means in ran
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ΩNull and ΩRange represents the
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Thus, calculation of the QR factori
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4.3.3 Algorithm for Feature Fusion
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The architecture of our proposed me
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T R T S denoted by RVNull and RVNul
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x DNull DRange DP (x) DNull(x) DRan
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C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
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iii) Project all class means onto n
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Table 4.1: Effect of increasing num
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Table 4.3: Effect of increasing num
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(Original face) (Null face) (Range
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Table 4.6: Performance of Dual Spac
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5.1 Introduction In recent years, t
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Page 125 and 126: validation1, validation2 and test.
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Page 157 and 158: (a) (b) Figure A.14: Thinned images
Page 159 and 160: (a) (b) Figure A.16: Thinned finger
Page 161 and 162: Paired Minutiae Minutiae with unmat
Page 163 and 164: Bibliography [1] FVC 2002 website.
Page 165 and 166: [25] Li-Fen Chen, Hong-Yuan Mark Li
Page 167 and 168: [49] Lin Hong, Yifei Wan, and Anil
Page 169 and 170: [71] L. Lam and C. Y. Suen. Applica
Page 171 and 172: IEEE Tran. on Pattern Analysis and
Page 173: of-the-Art Systems. IEEE Tran. on P
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