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1 Introduction:
Face recognition is today's hottest R & D direction, with a very wide range of applications in security, finance, tourism and other fields. This book is comprehensive, systematic introduction to "brush face" behind the technology, including face detection, face recognition, face retrieve relevant principles of algorithms and implementation techniques. This book explains the algorithm has a high degree of operability and usability. Through studying this book, researchers, engineers can within 3 to 5 months, the system understand and master the face detection, face recognition, face retrieves the relevant principles and techniques. This book is innovative, clear layer, for university teachers, researchers, graduate students, advanced undergraduates, face recognition enthusiasts.
2 of Contents:
Chapter 1, face detection, face recognition and retrieval Overview 1
1.1 face detection, face recognition and retrieval of application scenarios 2
1.1.1 current application 3
1.1.2 The future application 5
1.2 Face detection, retrieved face recognition and face common data set. 5
1.2.1 LFW dataset. 5
1.2.2 FDDB dataset. 6
1.2.3 Wanwan1 dataset. 7
1.2.4 8 Wanwan2 dataset
Introduction 1.3 OpenCV, installation and use of 8
References 15
section 2 16 image processing
basic concept of digital image processing 16 2.1
2.1.1 17 pixels
2.1.2 resolution 17
2.1.3 hue, brightness and saturation 19
contrast image 22 2.1.4
2.1.5 image texture 23
2.2 color space 26 is
2.2.1 the RGB color space 26 is
2.2.2 the HSV color space 27
2.2.3 the YUV color space 27
2.2.4 28 color space conversion
basic operation of digital image processing 32 2.3
2.3.1 read image take 32
2.3.2 34 to display the image
modified image 35 2.3.3
2.3.4 36 stored image
2.3.5 obtain basic information image 37
2.4 38 converts the image type and
2.4.1 picture type 39
2.4.2 39 converts the image type
2.5 image conversion processing 48
2.5.1 translation image 48
2.5.2 rotating the image 51
2.5. scaling the image 3 52
2.5.4 cutout image 55
2.5.5 flipped image 58
2.6 60 filtering image noise and
2.6.1 common noise model 60
2.6.2 classical denoising algorithm 64
Chapter 3 face detection combat 67
3.1 DPM face detection algorithm 67
3.1.1 DPM 68 people use face detection algorithm
principle 3.1.2 DPM face detection algorithm 69
detection results 3.1.3 DPM face detection algorithm 73
3.2 LAEO face detection algorithm 74
3.2.1 LAEO human face detection algorithm 74 using
the principles 3.2.2 LAEO face detection algorithm 75
3.2.3 LAEO detection result of the face detection algorithm 77
3.3 Viola & Jones face detection algorithm 79
3.3.1 Viola & Jones face detection algorithm use 79
principles 3.3.2 Viola & Jones face detection algorithm 79
3.3.3 detection result Viola & Jones face detection algorithm 82
References 83
Chapter 484 face detection algorithm based on the depth of learning
4.1 84 Facial Point Detection face detection algorithm CNN
85 4.1.1 Use Facial Point Detection Face detection algorithm CNN
4.1.2 CNN Facial Point Detection Face Detection Algorithm principle 85
4.1.3 CNN detection results facial Point detection face detection algorithm 86
4.2 DDFD face detection algorithm 87
using 87 4.2.1 DDFD face detection algorithm
4.2.2 DDFD face detection algorithm principle 88
4.2.3 detection result DDFD face detection algorithm 89
4.3 face detection algorithm fusion 90
References 92
Chapter 5 detector 94 based Fast R-CNN face
5.1 Fast R-CNN Profile 94
5.2 Fast R-CNN features and structures 95
5.3 Fast R-CNN using 96
pre-set data 5.4 97
5.5 98 EdgeBoxes using
5.6 EdgeBoxes extracted using 99 Proposal Object
5.7 Fast R-CNN-based face detection network model training and test 100
5.7.1 100 training phase
5.7.2 testing phase 106
5.7.3 evaluation phase 108
5.7.4 Optimization stage 111
References 112
Chapter 6 Recognition combat 113
6.1 114 DeepID algorithm
principle 6.1.1 DeepID algorithm 114
processes 6.1.2 DeepID algorithm 116
results 6.1.3 DeepID algorithm 126
6.2 Face VGG the Descriptor Algorithm 128
6.2.1 arithmetic VGG the Descriptor Face 128
implement 129 6.2.2 VGG Face Descriptor algorithm
results 6.2.3 VGG Face Descriptor algorithm 131 of
three kinds of face recognition algorithms 6.3 OpenCV 132
6.3.1 132 Eigenfaces
6.3.2 140 Fisherfaces
6.3.3 Histograms the Local Binary Patterns 148
Comparative analysis 6.4 recognition algorithm 152
6.5 Summary 153
References 155
Chapter 7 face search practices 157
7.1 157 face profile retrieval
method of calculating a face similarity 7.2 158
7.2.1 Euclidean distance of 159
7.2.2 cosine similarity 159
7.3 query processing algorithms 161
7.4 evaluation standard face 161 search results
7.5 PHash algorithm 161
162 7.5.1 PHash use algorithms
7.5.2 PHash algorithm principle 162
7.5.3 phash algorithm 162
7.5.4 phash algorithm experimental data, test results and analysis 164
7.6 DHash algorithm 168
7.6.1 DHash algorithm use 168
7.6.2 DHash principle algorithm 168
algorithm 7.6.3 DHash 169
7.6.4 DHash experimental data algorithm, the experimental results and analysis 170
7.7 PCA algorithm 173
173 7.7.1 using the PCA algorithm
principle 7.7.2 PCA algorithm 174
7.7 .3 PCA algorithm 175
7.7.4 the PCA algorithm experimental data, test results and analysis 177
7.8 BoF features 181
7.8.1 182 using BoF-SIFT algorithm
7.8.2 BoF-SIFT algorithm principle 182
7.8.3 BoF-SIFT algorithm achieve 182
7.8.4 BoF-SIFT algorithm experimental data, test results and analysis 188
7.9 KD-Tree index for fast retrieval of images 190
7.9.1 use FLANN algorithm 191
7.9.2 KD-Tree creation and query processing 191
7.9.3 FLANN of the KD-Tree algorithm 192
Experimental data 7.9.4 FLANN algorithm, the experimental results and analysis 194
195 7.10 Gabor algorithm
196 using 7.10.1 Gabor algorithm
7.10.2 Gabor algorithm principle 196
algorithm Gabor 7.10.3 199
7.10.4 Gabor algorithm experimental data, experimental results and analysis 204
7.11 208 the HOG algorithm
using the algorithm 209 7.11.1 HOG
principles 7.11.2 HOG algorithm 209
7.11.3 the HOG algorithm 210
experimental data 7.11.4 HOG algorithm, experimental results and analysis 212
7.12 215 learning feature depth
7.12 215 .1 depth using learning algorithms
7.12.2 principle deep learning algorithm 215
7.12.3 deep learning algorithm 216
7.12.4 depth learning algorithm experimental data, and analysis results 216
References 220
Chapter 8 face detection software application example 222 and
8.1 VeriLook face detection of commercial software 222
8.2 face face detection software 226 of
Comparative face detection algorithm 229 analyzes the various 8.3
8.4 video face detection and tracking 231
Ref 234