Master Thesis - Department of Computer Science

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(a) (b) (c) (d) Minutia Figure A.1: (a) and (b) A local window containing ridge valley structures; (c) and (d) Curve (i.e. x-signature) obtained along the direction normal to the ridge orientation. image is divided into a set of W × W nonoverlapping blocks and a single local ridge orientation is defined for each block. • A frequency image (F ): A frequency image, F , is an N × N image, where F (i, j) represents the local ridge frequency of the ridge and valley structures in a local neighborhood along a direction normal to the local ridge orientation. The ridge and valley structures in a local neighborhood where minutiae or singular points appear do not form a well-defined sinusoidal-shaped wave (see Fig. A.1). In such situations, the frequency is defined as the average frequency in the neighborhood of block (i, j). Like orientation image, frequency image is specified block-wise. A.1.2 Normalization Let I(i, j) denote the gray-level value at pixel (i, j), M and V AR denote the estimated mean and variance of I, respectively, and G(i, j) denote the normalized gray-level value at pixel (i, j). The normalized image is defined as follows: ⎧ ⎪⎨ M0 + G(i, j) = ⎪⎩ � V AR0(I(i,j)−M) 2 V AR � V AR0(I(i,j)−M) M0 − 2 V AR 124 if I(i, j) > M Otherwise ⎫ ⎪⎬ ⎪⎭ (A.3)
(a) (b) Figure A.2: Input fingerprint images. (a) (b) Figure A.3: Normalized images of two input fingerprints shown in Fig. A.2. where M0 and V AR0 are the desired mean and variance values, respectively. Nor- malization is a pixel-wise operation. It does not change the clarity of the ridge and valley structures. It reduces the variations in gray-level values along ridges and valleys, which facilitates the subsequent processing steps. Fig A.3 shows the result of normalization on two different fingerprint images shown in Fig A.2. A.1.3 Orientation Image The orientation image represents an intrinsic property of the fingerprint images and defines invariant coordinates for ridges and valleys in a local neighborhood. By view- ing a fingerprint image as an orientated texture, a least mean square orientation estimation algorithm is given below: 1. Divide G into blocks of size W × W (16 × 16). 125
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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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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
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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
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Page 125 and 126: validation1, validation2 and test.
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Page 135 and 136: LDA and LDA produce the same accura
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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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