Master Thesis - Department of Computer Science

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Face, Hand geometry, and Retina Multiple Biometrics Multiple Represen− tations Minutiae−based and Ridge map−based Representations Multiple Sensors Multibiometric Sources Multiple Units Right eye and Left Eye Solid−state and optical Sensors Multiple Instances Centerlight, Rightlight and Wink Face Figure 2.10: Sources of multiple evidence in multimodal biometrics. matchers: (a) Abstract level, (b) Rank level and (c) Measurement level. Fig. 2.11 shows the summary of approaches to information fusion in biometry. 2.3.3.1 Fusion Prior to Matching • Sensor level: Integration of information at sensor level is performed prior to matching. The raw data from the sensor(s) are combined in sensor level fusion [52]. This type of fusion can be done only if the multiple cues are either instances of the same biometric trait obtained from multiple compatible sensors or multiple instances of the same biometric trait obtained using a single sensor. 36
Prior to Matching Sensor Level Feature Level Abstract Level (i) Majority Voting (ii) Behavior Knowledge Space (iii) AND Rule (iv) OR Rule (i) Weighted Summation (ii) Concatenation Information Fusion in Biometrics (i) Sum Rule (ii) Product Rule (iii) Max Rule (iv) Min Rule (v) Weighted Sum Combination Approach Dynamic Classifier Selection After matching Rank Level Measurement Level (i) Highest Rank (ii) Borda Count (iii) Logistic Regression Class−conscious Approach Class−indifferent Approach (i) Decision Template (DT) (ii) Demster−Shafer (DS) (iii) Neural Network (NN) (i) k−NN (vi) Logistic Classifier (LOG) Decision Level Fusion (ii) Decision Trees (iii) SVM Classification Approach (iv) Linear Discriminant Classifier (LDC) (v) Quadratic Discriminant Classifier (QDC) Figure 2.11: Summary of approaches to information fusion in biometric systems. For example, the face images obtained from several cameras can be combined to form a 3D model of the face. • Feature level: Feature level fusion refers to combining different feature vectors that are obtained from one of the following sources: multiple sensors for the same biometric trait, multiple instances of the same biometric trait, multiple units of the same biometric trait or multiple biometric traits. When feature vectors are homogeneous, a single resultant feature vector can be calculated as a weighted average of the individual feature vectors. In case of non-homogeneous features, we can concatenate them to form a single feature vector which is not possible for incompatible feature sets. Attempts by Kumar et al. [66] in combining palmprint and hand-geometry features and by Ross and Govindarajan 37
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DEVELOPMENT OF EFFICIENT METHODS FO
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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
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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 51 and 52: according to the local orientation
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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 91 and 92: to our proposed method. Section 4.2
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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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