- 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 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