Master Thesis - Department of Computer Science

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Figure 2.6: (a) and (c) are Input Images; (b) and (d) are enhanced recoverable regions superimposed on corresponding input images. have optimal joint resolution in both spatial and frequency domains [32]. Examples of fingerprint enhancement with Gabor filtering as proposed by Hong, Wan , and Jain are shown in Fig 2.6. 2.2.2 Feature Representation and Feature Extraction A good fingerprint representation should have the following two properties: saliency and suitability. Saliency means that a representation should contain distinctive infor- mation and suitability means that the representation can be easily extracted, stored in a compact fashion, and be useful for matching. The fingerprint pattern, when analyzed at different scales, exhibit different features. • At global level: The ridge line flow, singular points (loop and delta), external fingerprint shape, orientation image, frequency image. • At local level: Minutiae (ridge termination and ridge bifurcation). • At very-thin level: Intra-ridge details (sweat pores). An elegant and practical method based on the Poincare index was proposed by Kawagoe and Tojo [58]. Let G be a vector field and C be a curve immersed in G. 28
Figure 2.7: Examples of minutiae; A minutia can be characterized by position and orientation. Then the Poincare index PG,C is defined as the total rotation of the vectors of G along C. An interesting implementation of Poincare method for locating singular points was proposed by Bazen and Gerez [11]. According to Green’s Theorem, closed line integral over a vector field can be calculated as a surface integral over the rotation of this vector field. In practice, instead of summing angle differences along a closed path, they compute the “rotation” of the orientation image and then perform a local integration over a small neighborhood of each element. Based on the observation that only a limited number of singularities can be present in a fingerprint, Karu and Jain [57] proposed to iteratively smooth the orientation image until a valid number of singularities is detected by Poincare index. Srinivasan and Murthy [117] extract singularities according to the local histogram of the orientation image. Koo and Kot [63] used multi-resolution approach to determine the singularities with single pixel accuracy. Most automatic systems for fingerprint comparison are based on minutiae matching. Hence, reliable minutiae extraction is an extremely important task and lot of search has been devoted to this topic. An example of minutiae is given in Fig 2.7. In some approaches minutiae are extracted on a thinned image which is obtained after binarization process. Some others have proposed minutiae extraction approaches that work directly on the gray-scale images without binarization and thinning. After obtaining binary skeleton, a simple image scan allows the pixel corresponding to minutiae to be selected. Pixels corresponding to minutiae are characterized by a crossing 29
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 and 46: Figure 2.5: A fingerprint image wit
Page 47: features (gradient coherence, inten
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
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Page 95 and 96: project only the class means in ran
Page 97 and 98: 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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