Master Thesis - Department of Computer Science

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area (foreground) from the image background. Separating the fingerprint area is use- ful to avoid extraction of features in noisy areas of the fingerprint and background. It is desirable that the background and foreground regions be identified at the earliest possible stage so that the subsequent processing can effectively concentrate on the foreground region of the image. Thus segmentation prior to other steps saves processing time and cost. Furthermore, it enhances the performance of feature extraction modules. Because fingerprint images are striated patterns, using global or local threshold- ing technique [43] does not allow the fingerprint area to be effectively isolated. In fingerprint, what really discriminates foreground and background is not the average image intensities but the presence of a striped and oriented pattern in the foreground and of an isotropic pattern (which does not have a dominant direction) in the background. In practice, the presence of noise (due to the dust on the surface of live scan fingerprint scanners) requires more robust segmentation techniques. Mehre et al. [89] isolated the fingerprint area according to local histograms of ridge orientations. Ridge orientation is estimated at each pixel and a histogram is computed for each 16 × 16 block. The presence of a significant peak in a histogram denotes an oriented pattern, whereas a flat or near-flat histogram is characteristic of an isotropic signal. The above method fails when a perfectly uniform block is encountered (e.g. a while block in background) because no local ridge orientation may be found. To deal with this case Mehre and Chatterjee [88] proposed a composite method that, besides histograms of orientations, computes the gray-scale variance blocks and, in the absence of reliable information from the histograms, assigns the low-variance blocks to the background. Ratha, Chen, and Jain [101] assigned each 16 × 16 block to the foreground or the background according to the variance of gray-levels in the orthogonal direction to the ridge orientation. They also derive a quality index from the block variance. The underlying assumption is that the noisy regions have no directional dependence, whereas regions of interest exhibit a very high variance in a direction orthogonal to the orientation of ridges and very low variance along ridges. Bazen and Gerez [10] proposed a pixel-wise segmentation technique, where three 26
features (gradient coherence, intensity mean, and intensity variance) are computed for each pixel, and a linear classifier associates the pixel with the background or the foreground. A supervised technique is used to learn the optimal parameters for the linear classifier for each specific acquisition sensor. A final morphological post-processing step [43] is performed to eliminate holes in both the foreground and background and to regularize the external silhouette of the fingerprint area. Their experimental results showed that this method provides accurate results; however, it’s computational complexity is markedly higher that most of the previously described block-wise approaches. 2.2.1.2 Image Enhancement The goal of an enhancement algorithm is to improve the clarity of the ridge structures in the recoverable regions and mark the unrecoverable regions as too noisy for further processing. The most widely used technique technique for fingerprint image enhancement is based on contextual filters. In contextual filtering, the filter characteristics change according to the local context. In fingerprint enhancement, the context is often defined by the local ridge orientation and local ridge frequency. The methods proposed by O’ Gorman and Nickerson [90, 91] was one of the first to use contextual filtering. They defined a mother filter based on four main parameters of fingerprint images at a given resolution; minimum and maximum ridge width, and minimum and maximum valley width. The local ridge frequency is assumed constant and therefore, the context is defined only by the local ridge orientation. Sherlock, Monro, and Millard [110, 111] performed contextual filtering in the Fourier domain. The filter defined in the frequency domain is the function: H(ρ, θ) = Hradial(ρ).Hangle(θ), (2.19) where Hradial depends only on the local ridge spacing ρ = 1/f and Hangle depends only on the local ridge orientation θ. Both Hradial and Hangle are defined by bandpass filters. Hong, Wan, and Jain [49] proposed an effective method based on Gabor filters. Gabor filters have both frequency-selective and orientation-selective properties and 27
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: Figure 2.5: A fingerprint image wit
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
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ΩNull and ΩRange represents the
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Thus, calculation of the QR factori
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4.3.3 Algorithm for Feature Fusion
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The architecture of our proposed me
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T R T S denoted by RVNull and RVNul
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x DNull DRange DP (x) DNull(x) DRan
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C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
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iii) Project all class means onto n
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Table 4.1: Effect of increasing num
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Table 4.3: Effect of increasing num
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(Original face) (Null face) (Range
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Table 4.6: Performance of Dual Spac
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5.1 Introduction In recent years, t
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This means for a classifier D: r i
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validation1, validation2 and test.
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5.3.1 The Algorithm The steps of ou
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5.4 Experimental Results In this ch
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sets. • We put as much as possibl
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argument is invalid in the context
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LDA and LDA produce the same accura
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space. If the training data uniform
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face for each subject, and compare
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also be introduced. But class speci
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These stages are discussed in the f
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(a) (b) Figure A.2: Input fingerpri
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(a) (b) Figure A.4: Orientation ima
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(i) For each block centered at (i,
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(a) (b) Figure A.7: Enhanced images
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(a) (b) Figure A.10: (a) and (b) sh
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(a) (b) Figure A.12: Binarized imag
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(a) (b) Figure A.14: Thinned images
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(a) (b) Figure A.16: Thinned finger
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Paired Minutiae Minutiae with unmat
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Bibliography [1] FVC 2002 website.
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[25] Li-Fen Chen, Hong-Yuan Mark Li
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[49] Lin Hong, Yifei Wan, and Anil
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[71] L. Lam and C. Y. Suen. Applica
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IEEE Tran. on Pattern Analysis and
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of-the-Art Systems. IEEE Tran. on P
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and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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