Master Thesis - Department of Computer Science

More documents

Recommendations

Info

5.9 Combined performance (in Percentage Accuracy) with ORL and F PA databases for different decision fusion strategies. . . . . . . . . . . . . 115 5.10 Combined performance (in Percentage Accuracy) with ORL and F PB databases for different decision fusion strategies. . . . . . . . . . . . . 116 A.1 Accuracy (in Percentage Accuracy) for DBA and DBB. . . . . . . . . 142 xiii
List of Figures 2.1 Summary of approaches to face recognition. . . . . . . . . . . . . . . 12 2.2 The concept of PCA. (a) Solid lines: The original basis; Dashed lines: The PCA basis; Geometric interpretation of principal eigenvectors illustrated in 2D space. (b) The projection (1D reconstruction) of the data using the first principal component. . . . . . . . . . . . . . . . 13 2.3 An example of PCA and LDA projection for a two class problem. . . 15 2.4 (a) Decomposition of R M into the principal subspace F and its orthogonal component ¯ F for a Gaussian density. (b) A typical eigenvalue spectrum and its division into the two orthogonal subspaces. . . . . 17 2.5 A fingerprint image with the core and four minutiae points marked on it. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.6 (a) and (c) are Input Images; (b) and (d) are enhanced recoverable regions superimposed on corresponding input images. . . . . . . . . . 28 2.7 Examples of minutiae; A minutia can be characterized by position and orientation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.8 (a) Intra-ridge pixel; (b) Termination minutia; (c) Bifurcation minutia 30 2.9 Different architectures of multimodal biometric system; (a) Parallel, (b) Serial and (c) Hierarchical. . . . . . . . . . . . . . . . . . . . . . 34 2.10 Sources of multiple evidence in multimodal biometrics. . . . . . . . . 36 2.11 Summary of approaches to information fusion in biometric systems. . 37 2.12 Operation of class-conscious methods. . . . . . . . . . . . . . . . . . . 42 2.13 Operation of class-indifferent methods. . . . . . . . . . . . . . . . . . 42
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: 4.1 Effect of increasing number of
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 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?