Face Detection and Recognition: Theory and Practice

350 Pages·2015·10.38 MB·English

by  Asit Kumar Datta

Face Detection
and Recognition

Theory and Practice
TThhiiss ppaaggee iinntteennttiioonnaallllyy lleefftt bbllaannkk
Face Detection
and Recognition

Theory and Practice
Asit Kumar Datta
University of Calcutta, Kolkata, India
Madhura Datta
University of Calcutta, Kolkata, India
Pradipta Kumar Banerjee
Future Institute of Engineering and Management,
Kolkata, India
Boca Raton London New York
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Contents
List of Figures xv
List of Tables xxi
Preface xxiii
Acknowledgment xxv
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Biometric identity authentication techniques . . . . . . . . . 2
1.3 Face as biometric identity . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Automated face recognition system . . . . . . . . . . . 5
1.3.2 Process flow in face recognition system . . . . . . . . . 7
1.3.3 Problems of face detection and recognition . . . . . . 11
1.3.4 Liveness detection for face recognition . . . . . . . . . 12
1.4 Tests and metrics . . . . . . . . . . . . . . . . . . . . . . . . 13
1.5 Cognitive psychology in face recognition . . . . . . . . . . . . 16
2 Face detection and recognition techniques 19
2.1 Introduction to face detection . . . . . . . . . . . . . . . . . 19
2.2 Feature-based approaches for face detection . . . . . . . . . . 21
2.2.1 Low-level analysis . . . . . . . . . . . . . . . . . . . . 22
2.2.1.1 Edges . . . . . . . . . . . . . . . . . . . . . . 22
2.2.1.2 Gray-level analysis . . . . . . . . . . . . . . . 23
2.2.1.3 Color information in face detection. . . . . . 23
2.2.1.4 Motion-based analysis . . . . . . . . . . . . . 24
2.2.2 Active shape model . . . . . . . . . . . . . . . . . . . 25
2.2.3 Feature analysis . . . . . . . . . . . . . . . . . . . . . 26
2.2.4 Image-based approaches for face detection . . . . . . . 27
2.2.5 Statistical approaches . . . . . . . . . . . . . . . . . . 28
2.3 Face recognition methods . . . . . . . . . . . . . . . . . . . . 28
2.3.1 Geometric feature-based method . . . . . . . . . . . . 29
2.3.2 Subspace-based face recognition . . . . . . . . . . . . 29
vii
viii Contents
2.3.3 Neural network-based face recognition . . . . . . . . . 31
2.3.4 Correlation-based method . . . . . . . . . . . . . . . . 32
2.3.5 Matching pursuit-based methods . . . . . . . . . . . . 33
2.3.6 Support vector machine approach . . . . . . . . . . . 33
2.3.7 Selected works on face classifiers . . . . . . . . . . . . 34
2.4 Face reconstruction techniques . . . . . . . . . . . . . . . . . 34
2.4.1 Three-dimensional face recognition . . . . . . . . . . . 35
2.4.1.1 Feature extraction . . . . . . . . . . . . . . . 38
2.4.1.2 Global feature extraction . . . . . . . . . . . 38
2.4.1.3 Three-dimensional morphable model . . . . . 39
3 Subspace-based face recognition 41
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2 Principal component analysis . . . . . . . . . . . . . . . . . . 42
3.2.1 Two-dimensional principal component analysis . . . . 46
3.2.2 Kernel principal component analysis . . . . . . . . . . 47
3.3 Fisher linear discriminant analysis . . . . . . . . . . . . . . . 49
3.3.1 Fisher linear discriminant analysis for two-class case . 53
3.4 Independent component analysis . . . . . . . . . . . . . . . . 58
4 Face detection by Bayesian approach 63
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 Bayes decision rule for classification . . . . . . . . . . . . . . 63
4.2.1 Gaussian distribution . . . . . . . . . . . . . . . . . . 64
4.2.2 Bayes theorem . . . . . . . . . . . . . . . . . . . . . . 69
4.2.3 Bayesian decision boundaries and discriminant function 70
4.2.4 Density estimation using eigenspace decomposition . . 72
4.3 Bayesian discriminant feature method . . . . . . . . . . . . . 75
4.3.1 Modelling of face and non-face pattern . . . . . . . . . 76
4.3.2 Bayes classification using BDF . . . . . . . . . . . . . 78
4.4 Experiments and results . . . . . . . . . . . . . . . . . . . . . 80
5 Face detection in color and infrared images 83
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.2 Face detection in color images . . . . . . . . . . . . . . . . . 84
5.3 Color spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.3.1 RGB model . . . . . . . . . . . . . . . . . . . . . . . . 85
5.3.2 HSI color model . . . . . . . . . . . . . . . . . . . . . 87
5.3.3 YCbCr color space . . . . . . . . . . . . . . . . . . . . 87
5.4 Face detection from skin regions . . . . . . . . . . . . . . . . 89
5.4.1 Skin modelling . . . . . . . . . . . . . . . . . . . . . . 89
Contents ix
5.4.1.1 Skin color modelling explicitly from RGB
space . . . . . . . . . . . . . . . . . . . . . . 89
5.4.1.2 Skin color modelling explicitly from YCbCr
space . . . . . . . . . . . . . . . . . . . . . . 89
5.5 Probabilistic skin detection . . . . . . . . . . . . . . . . . . . 90
5.6 Face detection by localizing facial features . . . . . . . . . . 92
5.6.1 EyeMap . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.6.2 MouthMap . . . . . . . . . . . . . . . . . . . . . . . . 94
5.7 Face detection in infrared images . . . . . . . . . . . . . . . . 97
5.8 Multivariate histogram-based image segmentation . . . . . . 98
5.8.1 Method for finding major clusters from a multivariate
histogram . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.8.2 ExperimentsandresultsonthecolorandIRfaceimage
datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.8.3 Utility of facial features . . . . . . . . . . . . . . . . . 103
6 Intelligent face detection 107
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.2 Multilayer perceptron model . . . . . . . . . . . . . . . . . . 108
6.2.1 Learning algorithm . . . . . . . . . . . . . . . . . . . 110
6.3 Face detection networks . . . . . . . . . . . . . . . . . . . . . 113
6.4 Training images . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.4.1 Data preparation . . . . . . . . . . . . . . . . . . . . . 113
6.4.2 Face training . . . . . . . . . . . . . . . . . . . . . . . 115
6.4.2.1 Active learning . . . . . . . . . . . . . . . . . 116
6.4.3 Exhaustive training . . . . . . . . . . . . . . . . . . . 117
6.5 Evaluation of face detection for upright faces . . . . . . . . . 118
6.5.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 118
6.5.2 Image scanning and face detection . . . . . . . . . . . 119
7 Real-time face detection 123
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.2 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
7.3 Integral image . . . . . . . . . . . . . . . . . . . . . . . . . . 125
7.3.1 Rectangular feature calculation from integral image . 125
7.4 Adaboost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.4.1 Modified AdaBoost algorithm . . . . . . . . . . . . . . 129
7.4.2 Cascade classifier . . . . . . . . . . . . . . . . . . . . . 131
7.5 Face detection using OpenCV . . . . . . . . . . . . . . . . . 133