Master Thesis - Department of Computer Science

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[81] D. Maio and D. Maltoni. An Efficient Approach to On-Line Fingerprint Veri- fication. In Int. Symp. on Artificial Intelligence (8th), pages 132–138, 1995. [82] D. Maio and D. Maltoni. Direct Gray-scale Minutiae Detection in Fingerprints. IEEE Tran. on Pattern Analysis and Machine Intelligence, 19(1), 1997. [83] Stephane G. Mallat. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Tran. on Pattern Analysis and Machine Intel- ligence, 11(7):674–693, Jul. 1989. [84] R. J. Mammone. Artificial Neural Networks for Speech and Vision. Chapman and Hall, Cambridge, 1993. [85] S. Marcelja. Mathematical Description of the Responses of Simple Cortical Cells. Journal of Optical Society of America, 70(11):1297–1300, 1980. [86] Alex M. Martinez and Avinash C. Kak. PCA versus LDA. IEEE Tran. on Pattern Analysis and Machine Intelligence, 23(2):228–233, February 2001. [87] B. M. Mehre. Fingerprint Image Analysis for Automatic Identification. Ma- chine Vision and Applications, 6(2):124–139, 1993. [88] B.M. Mehre and B. Chatterjee. Segmentation of Fingerprints Images - A Com- posite Approach. Pattern Recognition, 22(4):381–385, 1989. [89] B.M. Mehre, N. N. Murthy, S. Kapoor, and B. Chatterjee. Segmentation of Fin- gerprints Images using the Directional Image. Pattern Recognition, 20(4):429– 435, 1987. [90] L. O’Gorman and J. V. Nickerson. Matched Filter Design for Fingerprint Image Enhancement. In Int. Conf. on Acoustic Speech and Signal Processing, pages 916–919, 1988. [91] L. O’Gorman and J. V. Nickerson. An Approach to Fingerprint Filter Design. Pattern Recognition, 22(1):29–38, 1989. [92] E. Osuna, R. Freund, and F. Girosi. Training Support Vector Machines: An Application to Face Detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 193–199, 1997. [93] S. Pankanti, S. Prabhakar, and A. K. Jain. On the Individuality of Fingerprints. 150
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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
Page 119 and 120: Table 4.6: Performance of Dual Spac
Page 121 and 122: 5.1 Introduction In recent years, t
Page 123 and 124: This means for a classifier D: r i
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
Page 131 and 132: sets. • We put as much as possibl
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Page 135 and 136: LDA and LDA produce the same accura
Page 137 and 138: space. If the training data uniform
Page 139 and 140: face for each subject, and compare
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 and 166: [25] Li-Fen Chen, Hong-Yuan Mark Li
Page 167 and 168: [49] Lin Hong, Yifei Wan, and Anil
Page 169: [71] L. Lam and C. Y. Suen. Applica
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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