Master Thesis - Department of Computer Science

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[37] M. J. Er, S. Wu, and J. Lu. Face Recognition using Radial Basis Function (RBF) Neural Networks. In 38th Conference on Decision & Control, pages 2162–2167, Phoenix, Arizona USA, 1999. [38] N. J. Fliege. Multirate Digital Signal Processing. John Wiley and Sons, Ltd., 2004. [39] Keinosuke Fukunaga. Introduction to Statistical Pattern Recognition. Morgan Kaufmann, San Francisco, 2004. [40] C. Garcia, G. Zikos, and G. Tziritas. Wavelet Packet Analysis for Face Recog- nition. Image and Vision Computing, 18:289–297, 2000. [41] Nathalie George, Raymond J. Dolan, Gereon R. Fink, Gordon C. Baylis, Char- lotte Russell, and Jon Driver. Contrast Polarity and Face Recognition in the Human Fusiform Gyrus. Nature neuroscience, 2(6):574–580, Jun. 1999. [42] Gene H. Golub and Charles F. Van Loan. Matrix Computations. The Johns Hopkins University Press, Baltimore and London. [43] R. C. Gonzalez and R. E. Woods. Digital image processing, 2nd edition. Ad- disson Wesley, page 229, 1999. [44] S. Gutta, J. R. J. Huang, P. Jonathan, and H. Wechsler. Mixture of Experts for Classification of Gender, Ethnic Origin, and Pose of Human Faces. IEEE Trans. on Neural Networks, 11:948–959, July 2000. [45] R. Haung, Q. Liu, H. Lu, and S. Ma. Solving the Small Size Problem of LDA. In Proc. Int. Conf. on Pattern Recognition, volume 3, pages 29–32, Aug. 2002. [46] T. K. Ho, J. J. Hull, and S. N. Srihari. Decision Combination in Multiple Classifier Systems. IEEE Tran. on Pattern Analysis and Machine Intelligence, 16(1):66–75, Jan. 1994. [47] J. Hong and X. Tan. A New Approach to Point Pattern Matching. In Int. Conf. on Pattern Recognition (9th), pages 82–84, 1988. [48] Lin Hong and Anil Jain. Integrating Faces and Fingerprints for Personal Identification. IEEE Tran. on Pattern Analysis and Machine Intelligence, 20(12):1295–1307, December 1998. 146
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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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Page 119 and 120: Table 4.6: Performance of Dual Spac
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
Page 129 and 130: 5.4 Experimental Results In this ch
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Page 135 and 136: LDA and LDA produce the same accura
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Page 141 and 142: also be introduced. But class speci
Page 143 and 144: These stages are discussed in the f
Page 145 and 146: (a) (b) Figure A.2: Input fingerpri
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
Page 153 and 154: (a) (b) Figure A.10: (a) and (b) sh
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: [25] Li-Fen Chen, Hong-Yuan Mark Li
Page 169 and 170: [71] L. Lam and C. Y. Suen. Applica
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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