Master Thesis - Department of Computer Science

More documents

Recommendations

Info

suppressed and a detail subband (in some cases) is eliminated. Reconstructing a face with an optimal selection of subbands enhances the performance of face recognition. We present four different criteria as cost functions to obtain an optimal subband face for each subject, and compare their performances. The performance of the subband face representation with several linear subspace techniques: PCA, LDA, 2D-PCA, 2D-LDA and Discriminative Common Vec- tors (DCV), on Yale, ORL and PIE face databases show that the subband face based representation performs significantly better than that proposed by Ekenel for Multiresolution Face Recognition [36] for frontal face recognition, in the presence of varying illumination, expression and pose. • In the second approach, we propose a new face recognition technique by combining information from null space and range space of within-class scatter of a face space. The combination of information at feature level poses a problem of optimally merging two eigenmodels obtained separately from null space and range space. We use two different methods: 1) Covariance Sum and 2) Gramm- Schmidt Orthonormalization to construct a new combined space, named as dual space, by merging two different sets of discriminatory directions obtained from null space and range space separately. We employ forward or backward selection technique to select the best set of discriminative features from dual space and use them for face recognition. Experimental results on three public databases, Yale, ORL and PIE will show the superiority of our method over a face recognition technique called Discriminative Common Vectors (DCV) [20], based on only the null space of within-class scatter. • The third approach of face recognition presents a novel face recognition technique by combining information from null space and range space of within-class scatter of a face space at decision level. Along with two classical decision fusion strategies sum rule and product rule, we employ our own decision fusion technique which exploits each classifier space separately to enhance combined performance. Our method of decision fusion uses Linear Discriminant Anal- ysis (LDA) and nonparametric LDA on classifier’s response to enhance class separability. This method is also evaluated using Yale, ORL and PIE database. 6
• A new approach for multimodal biometry has been proposed based on a decision fusion technique to combine decisions from face and fingerprint classifiers. This process of decision fusion exploits the individual classifier space on the basis of availability of class specific information present in the classifier output space. The class specific contextual information is exploited using LDA and nonparametric LDA at the classifier response level to enhance class separability. Eventhough, face classifier is observed to provide contextual information, fingerprint classifier often does not provide this information due to high sensi- tivity of available minutiae points, producing partial matches across subjects. The enhanced face and fingerprint classifiers are combined using sum rule. We also propose a generalized algorithm for Multiple Classifier Combination (MCC) based on our approach. Experimental results exhibit the superiority of the proposed method over other existing fusion techniques like sum, product, max, min rules, decision template and Dempster-Shafer theory. 1.4 Overview of the Thesis In this thesis, the problem of face recognition has been attempted using three different techniques and then face and fingerprint information are combined to construct a multimodal biometric system. The rest of the thesis is organized in the following way. Chapter 2: Literature Review - This chapter discusses the techniques and recent developments in the field of face, fingerprint, and multiple classifier combination. A few methods based on linear transformation for face recognition are also described in this chapter. We also provide a vivid description of a set of methods for Multiple Classifier Combination (MCC). Chapter 3: An Efficient Method of Face Recognition using Subject-Specific Subband Faces - This chapter emphasizes on our proposed method of face recognition using Subband face representation. We propose four criteria and an algorithm for obtaining subject-specific subband faces and then integrate them separately with five linear subspace methods, namely, PCA, LDA, 2D-PCA, 2D-LDA and DCV. Results 7
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: • Spoof Attacks: An impostor may
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 and 48: features (gradient coherence, inten
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
Page 97 and 98:
ΩNull and ΩRange represents the
Page 99 and 100:
Thus, calculation of the QR factori
Page 101 and 102:
4.3.3 Algorithm for Feature Fusion
Page 103 and 104:
The architecture of our proposed me
Page 105 and 106:
T R T S denoted by RVNull and RVNul
Page 107 and 108:
x DNull DRange DP (x) DNull(x) DRan
Page 109 and 110:
C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
Page 111 and 112:
iii) Project all class means onto n
Page 113 and 114:
Table 4.1: Effect of increasing num
Page 115 and 116:
Table 4.3: Effect of increasing num
Page 117 and 118:
(Original face) (Null face) (Range
Page 119 and 120:
Table 4.6: Performance of Dual Spac
Page 121 and 122:
5.1 Introduction In recent years, t
Page 123 and 124:
This means for a classifier D: r i
Page 125 and 126:
validation1, validation2 and test.
Page 127 and 128:
5.3.1 The Algorithm The steps of ou
Page 129 and 130:
5.4 Experimental Results In this ch
Page 131 and 132:
sets. • We put as much as possibl
Page 133 and 134:
argument is invalid in the context
Page 135 and 136:
LDA and LDA produce the same accura
Page 137 and 138:
space. If the training data uniform
Page 139 and 140:
face for each subject, and compare
Page 141 and 142:
also be introduced. But class speci
Page 143 and 144:
These stages are discussed in the f
Page 145 and 146:
(a) (b) Figure A.2: Input fingerpri
Page 147 and 148:
(a) (b) Figure A.4: Orientation ima
Page 149 and 150:
(i) For each block centered at (i,
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-
show all

Master Thesis - Department of Computer Science

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?