Master Thesis - Department of Computer Science

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[61] T. Kohonen. Self-Organization and Associative Memory. Springer-Verlag, New York, 1988. [62] Hui Kong, Eam Khwang Teoh, Jian Gang Wang, and R Venkateswarlu. Two Dimensional Fisher Discriminant Analysis: Forget About Small Sample Size Problem. In International Conference on Acoustics Speech and Signal Process- ing, volume 2, pages 761–764, March 2005. [63] W. M. Koo and A. Kot. Curvature-Based Singular point Detection. In Int. Conf. on Audio- and Video-based Biometric Person authentication (3rd), pages 229–234, 2001. [64] Z. M. Kovacs-Vajna. A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping. IEEE Tran. on Pattern Analysis and Machine Intelligence, 22:1266–1276, 2000. [65] J. J. Kulikowski, S. Marcelja, and P. Bishop. Theory of Spatial Position and Spatial Frequency Relations in the Receptive Fields of Simple Cells in Visual Cortex. Biological Cybernetics, 43:187–198, 1982. [66] A. Kumar, D. C. M. Wong, H. C. Shen, and A. K. Jain. Personal Verification using Palmprint and Hand Geometry Biometrics. In Fourth International Con- ference on Audio- and Video-based Biometric Person Authentication (AVBPA), pages 669–678, Guildford, U.K., June 2003. [67] A. Pavan Kumar, V. Kamakoti, and Sukhendu Das. System-on-Programmable- Chip Implementation for Online Face Recognition. Accepted for Publication in the special issue in Pattern Recognition Letters, Jan 2006. [68] Ludmila I. Kuncheva, James C. Bezdek, and Robert P. W. Duin. Decision Templates for Multiple Classifier Fusion: An Experimental Comparison. Pat- tern Recognition, 34:299–314, 2001. [69] M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. Von ser Malburg, R. P. Wurtz, and W. Konen. Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Transactions on Computers, 42:300–310, March 1997. [70] L. Lam and C. Y. Suen. Optimal Combination of Pattern Classifiers. Pattern Recognition Letters, 16:945–954, 1995. 148
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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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Page 121 and 122: 5.1 Introduction In recent years, t
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Page 125 and 126: validation1, validation2 and test.
Page 127 and 128: 5.3.1 The Algorithm The steps of ou
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Page 141 and 142: also be introduced. But class speci
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Page 147 and 148: (a) (b) Figure A.4: Orientation ima
Page 149 and 150: (i) For each block centered at (i,
Page 151 and 152: (a) (b) Figure A.7: Enhanced images
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Page 155 and 156: (a) (b) Figure A.12: Binarized imag
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: [49] Lin Hong, Yifei Wan, and Anil
Page 171 and 172: IEEE Tran. on Pattern Analysis and
Page 173 and 174: of-the-Art Systems. IEEE Tran. on P
Page 175 and 176: and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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