Master Thesis - Department of Computer Science

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(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure A.8: (a), (b), (c), (d) and (e) shows blocks of size 16 × 16; (f), (g), (h), (i) and (j) shows the Gabor filters tuned to the orientation and frequency of the corresponding blocks. (a) (b) Figure A.9: (a) and (b) shows masks of two input fingerprints shown in Fig. A.2. 132
(a) (b) Figure A.10: (a) and (b) shows the enhanced fingerprint images after masking. segmentation. Firstly, the image is divided into blocks and the gray-level variance is calculated for each block in the image. If the variance is less than the global threshold, then the block is assigned to be a background region (i.e. assign R(i, j) = 0 for the block centered at (i, j)); otherwise, it is assigned to be the part of the foreground (i.e. assign R(i, j) = 1), where R is called the mask. The gray-level variance for a block of size W × W is defined as: V (k) = 1 W 2 W� −1 i=0 W� −1 j=0 (E(i, j) − M(k)) 2 . (A.29) where M(k) is the mean gray-level value for block k and E(i, j) is the enhanced image. Then, the enhanced image after masking can be represented by, ⎧ ⎫ ⎪⎨ ⎪⎬ E(i, j) if R(i, j) = 1 H(i, j) = (A.30) ⎪⎩ ⎪⎭ 255 Otherwise Fig. A.9 and Fig. A.10 shows the masks and enhanced images after masking for input fingerprints, respectively. A.3 Image Binarization Binarization is the process of converting a gray-level image into a binary image where the black pixels represent ridges and white pixels represent valleys. This process improve the contrast between the ridges and valleys and consequently facilitates the extraction of minutiae. 133
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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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Page 107 and 108: x DNull DRange DP (x) DNull(x) DRan
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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
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Page 135 and 136: LDA and LDA produce the same accura
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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: (a) (b) Figure A.7: Enhanced images
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-
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Master Thesis - Department of Computer Science

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