Master Thesis - Department of Computer Science

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Appendix A Fingerprint Recognition System Fingerprint is an important biometric signature to identify an individual. Fingerprint impression is time invariant after adolescence, unless the finger is damaged or bruised due to accidents or injuries. So, it finds wide applications for all type of secured access, including entry to buildings, missile launch control, nuclear reactor, bank vaults, confidential files and most recently, for e-Commerce accesses, such as, ATM counters, web-enabled transactions etc. A fingerprint is formed from an impression of the pattern of ridges on a finger. A ridge is defined as a single curved segment, and a valley is the region between two adjacent ridges. The minutiae, which are the local discontinuities in the ridge flow pattern, provide the features that are used for identification. Details such as the type, orientation, and location of minutiae are taken into account when performing minutiae extraction. The different stages of pre-processing for recognition of fingerprints are: 1. Image Enhancement (a) Normalization (b) Orientation Image Estimation (c) Frequency Image Estimation (d) Filtering 2. Image Segmentation 3. Binarization 4. Thinning 5. Minutiae Extraction 6. Minutiae Matching
These stages are discussed in the following sections. A.1 Image Enhancement The quality of the ridge structures in a fingerprint image is an important characteristic, as the ridges carry the information of characteristic features required for minutiae extraction. Ideally, in a well-defined fingerprint image, the ridges and valleys should alternate and flow in a locally constant direction. This regularity facilitates the de- tection of ridges and consequently, allows minutiae to be precisely extracted from thinned ridges. However, in practice, a fingerprint image may not be well defined due to the noise that corrupt the clarity of the ridge structures. Thus image enhancement are often employed to reduce the noise and enhance the definition of ridges against valleys. The fingerprint image enhancement algorithm receives an input gray-level fingerprint image and outputs the enhanced image after applying a set of intermediate steps which are: (a) Normalization, (b) Orientation Image Estimation, (c) Frequency Im- age Estimation and (d) Filtering using Gabor filter. This algorithm is mostly adopted from the technique proposed by Hong, Wan and Jain [49]. A.1.1 Notation • A gray-level Fingerprint image (I): A gray-level fingerprint image, I, is defined as an N ×N matrix, where I(i, j) represents the intensity of the pixel at the i th row and j th column. The mean and variance of a gray-level fingerprint image, I, are defined as, M(I) = 1 N 2 V AR(I) = 1 N 2 N−1 � i=0 N−1 � i=0 N−1 � j=0 N−1 � j=0 I(i, j) (A.1) (I(i, j) − M(I)) 2 (A.2) • An orientation image (O): An orientation image, O, is defined as an N ×N image, where O(i, j) represents the local ridge orientation at pixel (i, j). Local ridge orientation is usually specified for a block rather that at every pixel. An 123
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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’
Page 91 and 92: to our proposed method. Section 4.2
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Page 99 and 100: Thus, calculation of the QR factori
Page 101 and 102: 4.3.3 Algorithm for Feature Fusion
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Page 107 and 108: x DNull DRange DP (x) DNull(x) DRan
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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 119 and 120: Table 4.6: Performance of Dual Spac
Page 121 and 122: 5.1 Introduction In recent years, t
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Page 127 and 128: 5.3.1 The Algorithm The steps of ou
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Page 135 and 136: LDA and LDA produce the same accura
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Page 141: also be introduced. But class speci
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Page 147 and 148: (a) (b) Figure A.4: Orientation ima
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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-
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Master Thesis - Department of Computer Science

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