| Datta A.K. Face detection and recognition: theory and practice / A.K.Datta, M.Datta, P.K.Banerjee. - Boca Raton: CRC/Taylor & Francis, 2016. - xxv, 321 p.: ill., tab. - Bibliogr.: p.305-321. - Ind.: p.323-325. - ISBN 978-1-4822-2654-6 Шифр: И/З 973.2-D24) 02
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List of Figures ................................................. xv
List of Tables ................................................. xxi
Preface ...................................................... xxiii
Acknowledgment ................................................. xxv
1 Introduction ................................................ 1
1.1 Introduction ................................................ 1
1.2 Biometrie 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
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
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 Experiments and results on the color and IR face
image 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
8 Face space boundary selection for face detection
and recognition ........................................... 135
8.1 Introduction .............................................. 135
8.2 Face points, face classes and face space boundaries ....... 137
8.3 Mathematical preliminaries for set estimation method ...... 138
8.4 Face space boundary selection using set estimation ........ 140
8.4.1 Algorithm for global threshold-based face
detection .......................................... 140
8.5 Experimental design and result analysis ................... 142
8.5.1 Face/non-face classification using global
threshold during face detection .................... 142
8.5.2 Comparison between threshold selections by ROC-
based and set estimation-based techniques .......... 142
8.5.2.1 Formation of training-validation-test set ...... 143
8.6 Classification of face/non-face regions ................... 146
8.7 Class specific thresholds of face-class boundaries for
face recognition .......................................... 148
8.8 Experimental design and result analysis ................... 149
8.8.1 Description of face dataset ........................ 149
8.8.1.1 Recognition rates .............................. 151
8.8.2 Open test results considering imposters in the
system ............................................. 151
8.8.3 Recognition rates considering only clients in the
system ............................................. 153
9 Evolutionary design for face recognition .................. 161
9.1 Introduction .............................................. 161
9.2 Genetic algorithms ........................................ 162
9.2.1 Implementation ..................................... 163
9.2.2 Algorithm .......................................... 164
9.3 Representation and discrimination ......................... 165
9.3.1 Whitening and rotation transformation .............. 165
9.3.2 Chromosome representation and genetic operators .... 167
9.3.3 The fitness function ............................... 167
9.3.4 The evolutionary pursuit algorithm for face
recognition ........................................ 168
10 Frequency domain correlation filters in face recognition .. 171
10.1 Introduction .............................................. 172
10.1.1 PSR calculation .................................... 173
10.2 A brief review on correlation filters ................ 174
10.3 Mathematical background of correlation filter ............. 179
10.3.1 ECPSDF filter design ............................... 179
10.3.2 MACE filter design ................................. 181
10.3.2.1 Constrained optimization with Lagrange
multipliers ...................................... 182
10.3.3 MVSDF filter design ................................ 183
10.3.4 Optimal trade-off (OTP) filter design .............. 183
10.3.5 Unconstrained correlation filter design ............ 184
10.3.5.1 MACH filter design ............................ 184
10.3.5.2 UMACE filter design ........................... 187
10.3.5.3 OTMACH filter design .......................... 188
10.4 Physical requirements in designing correlation filters .... 188
10.5 Applications of correlation filters ....................... 190
10.6 Performance analysis ...................................... 193
10.6.1 Performance evaluation using PSR values ............ 194
10.6.2 Performance evaluation in terms of %RR and %FAR .... 195
10.6.3 Performance evaluation by receiver operating
characteristics (ROC) curves ....................... 204
10.7 Video correlation filter .................................. 205
10.8 Formulation of unconstrained video filter ................. 206
10.8.1 Mathematical formulation of MUOTSDF ................ 207
10.8.2 Unconstrained video fiher .......................... 209
10.9 Distance classifier correlation filter ............... 212
10.10 Application of UVF for face detection .................... 213
10.10.1 Training approach ................................. 213
10.10.2 Testing approach .................................. 213
10.10.3 Face detection in video using UVF ................. 217
10.10.3.1 Modification in training approach ............... 218
10.10.4 Validation of face detection ...................... 219
10.10.5 Face classification using DCCF .................... 219
11 Subspace-based face recognition in frequency domain ....... 223
11.1 Introduction .............................................. 224
11.2 Subspace-based correlation filter ......................... 224
11.3 Mathematical modeUing with ID subspace .................... 226
11.3.1 Reconstructed correlation filter using ID
subspace ........................................... 226
11.3.2 Optimum projecting image correlation filter using
ID subspace ........................................ 228
11.4 Face classification and recognition analysis in
frequency domain .......................................... 230
11.5 Test results with ID subspace analysis .................... 231
11.5.1 Comparative study in terms of PSRs ................. 231
11.5.2 Comparative study on %RR and %FAR .................. 232
11.6 Mathematical modelling with 2D subspace ................... 233
11.6.1 Reconstructed correlation filter using
2D subspace ........................................ 234
11.7 Test results on 2D subspace analysis ...................... 236
11.7.1 PSR value distribution for authentic and impostor
classes ............................................ 236
11.7.2 Comparative performance in terms of %RR ............ 236
11.7.3 Performance evaluation using ROC analysis .......... 238
11.8 Class-specific nonfinear correlation filter ............... 239
11.9 Formulation of nonlinear correlation filters .............. 240
11.9.1 Nonlinear optimum projecting image correlation
filter ............................................. 240
11.9.2 Nonlinear optimum reconstructed image correlation
filter ............................................. 243
11.10 Face recognition analysis using correlation classifiers .. 244
11.11 Test results ............................................. 245
11.11.1 Comparative study on discriminating performances .. 245
11.11.2 Comparative performance based on PSR
distribution ...................................... 246
11.11.3 Performance analysis using ROC .................... 248
11.11.4 Noise sensitivity ................................. 250
12 Landmark localization for face recognition ................ 253
12.1 Introduction .............................................. 253
12.2 Elastic bunch graph matching .............................. 253
12.3 Gabor wavelets ............................................ 254
12.4 Gabor jets ................................................ 257
12.5 The elastic bunch graph matching algorithm ................ 259
12.6 Application to face recognition ........................... 260
12.7 Facial landmark detection ................................. 261
12.7.1 ASEF correlation filter ............................ 261
12.7.2 Formulation of ASEF ................................ 262
12.8 Eye detection ............................................. 263
12.9 Multicorrelation approach ................................. 264
12.9.1 Design of landmark filter(LF) ...................... 264
12.9.2 Landmark locahzation with localization filter ...... 267
12.10 Test resuhs .............................................. 268
13 Two-dimensional synthetic face generation using set
estimation ................................................ 273
13.1 Introduction .............................................. 273
13.2 Generating face points from intraclass face images ........ 274
13.2.1 Face generation using algorithm with intraclass
features and related peak signal to noise ratio .... 274
13.3 Generating face points from interclass face images ........ 277
13.3.1 Face generation with interclass features ........... 280
13.3.2 Rejection of the non-meaningful face and
corresponding PSNR test ............................ 283
13.4 Generalization capability of set estimation method ........ 283
13.5 Test of significance ...................................... 286
14 Datasets of face images for face recognition systems ...... 291
14.1 Face datasets ............................................. 291
14.1.1 ORL dataset ........................................ 292
14.1.2 OULU physics dataset ............................... 292
14.1.3 XM2VTS dataset ..................................... 293
14.1.4 Yale dataset ....................................... 293
14.1.5 Yale-B dataset ..................................... 294
14.1.6 MIT dataset ........................................ 294
14.1.7 PIE dataset ........................................ 295
14.1.8 UMIST dataset ...................................... 295
14.1.9 PURDU AR dataset ................................... 295
14.1.10 FERET dataset ..................................... 296
14.1.11 Performance evaluation of face recognition
algorithms ........................................ 296
14.2 FERET and XM2VTS protocols ................................ 297
14.3 Face recognition grand challenge (FRGC) ................... 298
14.4 Face recognition vendor test (FRVT) ....................... 299
14.5 Multiple biometric grand challenge ........................ 300
14.6 Focus of evaluation ....................................... 301
Conclusion ................................................ 303
Bibliography ................................................... 305
Index .......................................................... 323
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