Master Thesis - Department of Computer Science

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algebraic methods for point pattern matching. The Hough transform-based approach [7] converts point pattern matching to the problem of detecting peaks in the Hough space of transformation parameters. Ratha et al. [102] proposed a Hough transform-based minutiae matching approach where the transformation parameters are displacement, rotation, and scale. Two quite atypical fingerprint matching approaches, classifiable as local minutiae matching, have been introduced by Maio and Maltoni [81] and by Kovacs-Vajna [64]. Both these techniques operate asymmetrically. Fingerprint enhancement and accu- rate minutiae extraction are performed only on the template fingerprint at enrollment time, resulting in the minutiae set T. During testing, the existence of a correspon- dence for each minutia in T is checked by locally searching the input fingerprint. 2.3 Recent Approaches to Multiple Classifier Combination In real-world applications, some limits of monomodal biometric systems have already been reported. Indeed some biometrics have only little variation over the population, have large intra-class variability over time, or/and are not present in all the population. To fill these gaps, the use of multimodal biometrics is a first choice solution. Multimodal biometric systems increase robustness and are more reliable. Multimodal approaches provide appropriate measures to resist against spoof attacks, as it is dif- ficult to counterfeit several modalities at the same time, to circumvent a system. They also provide an adopted solution to the limitations of universality, as even if a biometrics is not possessed by a person, the other(s) modality(ies) can still be used. Multimodal biometric systems that have been proposed in literature can be classi- fied based on four main parameters: (1) Architecture or operational mode, (2) Fusion scenarios, (3) Level of fusion and (4) Methods for integrating multiple cues. 2.3.1 Architecture or Operational Mode Architecture of multimodal biometric system refers to the sequence in which the multiple cue are acquired and processed. Multimodal biometric systems can operate in three different modes: (a) Parallel, (b) Serial and (c) Hierarchical (see in Fig. 2.9). 32
• Parallel Mode: This operational mode consists in completing the combination of the modalities simultaneously. Different modalities operate indepen- dently and their results are combined using an appropriate fusion scheme. • Serial Mode: This operational mode consists in completing the combination of the modalities one after the other, as it permits to reduce the population at each stage before the following modality is used. The decision could thus be taken before all the remaining biometrics are acquired, reducing consider- ably the processing duration. Here the outcome of one modality affects the processing of the subsequent modalities. • Hierarchical Mode: This operational mode consists in completing the combination of the modalities in a hierarchical scheme, like a tree structure, when the number of classifiers is large. This architecture can also allow the user to decide which modality he/she would present first. Finally, if the system is faced with the task of identifying the user from a large database, it can utilize the outcome of each modality to successively prune the database, thereby making the search faster and more efficient. An example of a cascaded multibiometric system is the one proposed by Hong and Jain in [48]. Most proposed multibiometric systems have a parallel architecture [115, 106]. But the choice of system architecture depends on the application. User-friendly and less security like ATMs can use hierarchical mode. On the other hand parallel mode are well suited for applications where security is of paramount importance (military installations). 2.3.2 Fusion Scenarios Multimodal biometric systems overcome some of the limitations of unimodal biometric systems by consolidating the evidence obtained from different sources (see Fig. 2.10). The sources are given below: • Single Biometry, Multiple Sensors: The same biometric is acquired by different sensors and combined to complete and improve the recognition process (e.g. optical and solid-state fingerprint sensors). The use of multiple sensors 33
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
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Page 41 and 42: faces of 40 subjects. • Others: A
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Page 47 and 48: features (gradient coherence, inten
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Page 51: according to the local orientation
Page 55 and 56: can address the problem of noisy se
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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 = |
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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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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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