Master Thesis - Department of Computer Science

More documents

Recommendations

Info

[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
[71] L. Lam and C. Y. Suen. Application of Majority Voting to Pattern Recognition: An Analysis of Its Behavior and Performance. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 27(5):553–568, 1997. [72] Andreas Lanitis, Christopher J. Taylor, and Timothy Francis Cootes. Auto- matic Interpretation and Coding of Face Images using Flexible Models. IEEE Tran. on Pattern Analysis and Machine Intelligence, 19(7):743–756, 1997. [73] S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back. Face Recognition: A Convolutional Neural Network Approach. IEEE Trans. on Neural Networks, 8(1):98–112, January 1997. [74] Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, and Andrew D. Back. Face Recognition: A Convectional Neural Network Approach. IEEE Trans. on Neu- ral Networks, 8(1):98–113, 1998. [75] Kuang-Chih Lee, Jeffrey Ho, and David J. Kriegman. Acquiring Linear Sub- spaces for Face Recognition under Variable Lighting. IEEE Tran. on Pattern Analysis and Machine Intelligence, 27(5):684–698, May 2005. [76] W. F. Leung, S. H. Leung, W. H. Lau, and A. Luk. Fingerprint Recognition using Neural Network. In Workshop Neural Network for Signal Processing, pages 226–235, 1991. [77] Ming Li and Baozong Yuan. 2D-LDA: A Statistical Linear Discriminant Anal- ysis for Image Matrix. Pattern Recognition Letters, 26:527–532, 2005. [78] Shang-Hung Lin, Sun-Yuan Kung, and Long ji Lin. Face Recognition/Detection by Probabilistic Decision-based Neural Network. IEEE Trans. on Neural Net- works, 8(1):114–131, January 1997. [79] Z. Liposcak and S. Loncaric. Face Recognition from Profiles using Morpho- logical Operations. In International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pages 47–52, 1999. [80] K. Liu, Y.Q. Cheng, and J.Y. Yang. Algebraic Feature Extraction for Image Recognition Based on an Optimal Discriminant Criterion. Pattern Recognition, 26(6):903–911, June 1993. 149
Page 1 and 2:
DEVELOPMENT OF EFFICIENT METHODS FO
Page 3 and 4:
To My Parents, Sisters and Dearest
Page 5 and 6:
Mirnalinee, Shreyasee, Lalit, Manis
Page 7 and 8:
LDC Linear Discriminant Classifier
Page 9 and 10:
3 An Efficient Method of Face Recog
Page 11 and 12:
A.6 Minutiae Matching . . . . . . .
Page 13 and 14:
4.1 Effect of increasing number of
Page 15 and 16:
List of Figures 2.1 Summary of appr
Page 17 and 18:
3.7 Area difference between genuine
Page 19 and 20:
Abstract Biometrics is a rapidly ev
Page 21 and 22:
CHAPTER 1 Introduction The issues a
Page 23 and 24:
1.1.1 Applications Biometrics has b
Page 25 and 26:
• Spoof Attacks: An impostor may
Page 27 and 28:
• A new approach for multimodal b
Page 29 and 30:
CHAPTER 2 Literature Review This re
Page 31 and 32:
ange [23], infrared scanned [137] a
Page 33 and 34:
u 2 x 2 u 1 x 1 (a) PCA basis (b) P
Page 35 and 36:
PCA Projection Class 1 Class 2 LDA
Page 37 and 38:
F DIFS DFFS F (a) (b) F 1 L Figure
Page 39 and 40:
image A of m rows and n columns is
Page 41 and 42:
faces of 40 subjects. • Others: A
Page 43 and 44:
malizing the vector components, the
Page 45 and 46:
Figure 2.5: A fingerprint image wit
Page 47 and 48:
features (gradient coherence, inten
Page 49 and 50:
Figure 2.7: Examples of minutiae; A
Page 51 and 52:
according to the local orientation
Page 53 and 54:
• Parallel Mode: This operational
Page 55 and 56:
can address the problem of noisy se
Page 57 and 58:
Prior to Matching Sensor Level Feat
Page 59 and 60:
determined by logistic regression.
Page 61 and 62:
Class-indifferent Methods • Decis
Page 63 and 64:
3. The final DS soft vector is calc
Page 65 and 66:
on three face databases and compare
Page 67 and 68:
for a face. This was illustrated by
Page 69 and 70:
Image Rows h(.) g(.) 2 2 Columns Fi
Page 71 and 72:
in the accuracy of face recognition
Page 73 and 74:
(a) (a1) (a2) (a3) (b) (b1) (b2) (b
Page 75 and 76:
Genuine Impostor Scores Scores 1 0
Page 77 and 78:
Error d1 = (1 − TZeroF RR) d2 = |
Page 79 and 80:
The algorithm for obtaining the sub
Page 81 and 82:
each subject, 42 samples (flashes f
Page 83 and 84:
Table 3.3: Peak Recognition Accurac
Page 85 and 86:
Table 3.5: Peak Recognition Accurac
Page 87 and 88:
Table 3.7: Performance of Ekenel’
Page 89 and 90:
Table 3.9: Performance of Ekenel’
Page 91 and 92:
to our proposed method. Section 4.2
Page 93 and 94:
plored both of the spaces to captur
Page 95 and 96:
project only the class means in ran
Page 97 and 98:
ΩNull and ΩRange represents the
Page 99 and 100:
Thus, calculation of the QR factori
Page 101 and 102:
4.3.3 Algorithm for Feature Fusion
Page 103 and 104:
The architecture of our proposed me
Page 105 and 106:
T R T S denoted by RVNull and RVNul
Page 107 and 108:
x DNull DRange DP (x) DNull(x) DRan
Page 109 and 110:
C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
Page 111 and 112:
iii) Project all class means onto n
Page 113 and 114:
Table 4.1: Effect of increasing num
Page 115 and 116:
Table 4.3: Effect of increasing num
Page 117 and 118: (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
Page 133 and 134: argument is invalid in the context
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: [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-
show all

Master Thesis - Department of Computer Science

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?