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面向人机交互的人脸表情识别技术(英文版) 版权信息
- ISBN：9787030674661
- 条形码：9787030674661 ; 978-7-03-067466-1
- 装帧：一般胶版纸
- 册数：暂无
- 重量：暂无
- 所属分类：>
面向人机交互的人脸表情识别技术(英文版) 内容简介
人机交互要实现自然和谐的交流，计算机对表情信息的获取及分析是很好重要的。现有的人脸表情识别方法多是从人脸识别演变而来，只是结合表情识别的特点而进行运用，虽然取得一定效果，仍不十分成熟。本专著在现有方法的基础上，拟对面向人机交互的人脸表情识别算法进行研究。主要研究内容包括：(1)根据视觉信息处理的分模块性原理，对彩色图像的恒常性问题进行研究，克服传统颜色恒常性的不适定问题。(2)为实现高效准确的人脸检测和面部特征定位，对多特征融合的人脸检测方法和面部特征点定位问题进行研究。(3)综合考虑人脸表情特征的重要性和提取的有效性，对表情特征提取方法进行研究。(4)无论保局投影还是正交保局投影都是无监督流形学习算法，未能很好地利用人脸样本的类别信息，求取的投影向量并非判别意义上的很优。因而考虑融入表情样本的类别信息，对有监督的正交保局投影表情识别算法进行研究，利用类别先验知识进一步提高其分类性能。
面向人机交互的人脸表情识别技术(英文版) 目录
Contents
Preface
Chapter 1 Introduction 1
1.1 Research Background and Significance 1
1.2 Overview of Facial Expression Recognition 4
1.3 Research Status at Home and Abroad 7
1.3.1 Typical Facial Expression Recognition Algorithms 7
1.3.2 Existing Problems in Facial Expression Recognition 13
1.4 Research Contents 14
Chapter 2 Color Constancy Algorithm for Color Image Under Complex Illumination Conditions 20
2.1 Introduction 20
2.2 Correction of Color Deviation 21
2.2.1 LoG Chrominance Edge Extraction 23
2.2.2 White Balance Adjustment 28
2.2.3 Experimental Results and Analysis 30
2.3 Luminance Enhancement 32
2.3.1 Retinex Enhancement Algorithm 32
2.3.2 Color Retention Enhancement Algorithm 35
2.3.3 Experimental Results and Analysis 40
2.4 Fractional Step Color Constancy Algorithm 42
2.5 Summary of This Chapter 45
Chapter 3 Face Detection Algorithm with Multi-feature Fusion 46
3.1 Introduction 46
3.2 Skin Color Area Detection 49
3.2.1 Common Color Space 49
3.2.2 Skin Color Clustering Analysis 55
3.2.3 Skin Color Area Detection Algorithm 57
3.3 AdaBoost Face Detection Combined with Skin Color Features 60
3.3.1 AdaBoost Algorithm 62
3.3.2 AdaBoost Face Detection Algorithm Combined with Skin Color Features 67
3.4 Face Authentication Based on Harris Corner Point Detection 69
3.4.1 Principles for Harris Corner Point Detection 69
3.4.2 Harris Corner Point Detection Algorithm 71
3.4.3 Face Authentication Based on Harris Corner Point Detection 73
3.5 Experimental Results and Analysis 74
3.6 Summary of This Chapter 76
Chapter 4 Facial Features Location 78
4.1 Introduction 78
4.2 Eye Location Based on the Robustness of Illuminated Expression 79
4.2.1 Multi-scale Self-quotient Image 80
4.2.2 Rough Location of Morphological Filtering 81
4.2.3 Precise Eye Location 85
4.2.4 Experimental Results and Analysis 86
4.3 Location System of Key Color Facial Feature Point 90
4.3.1 Overview of the System 90
4.3.2 Eyes Location 92
4.3.3 Mouth Location 95
4.3.4 Experimental Results and Analysis 97
4.4 Summary of This Chapter 99
Chapter 5 Normalization of Facial Expression Image 101
5.1 Introduction 101
5.2 Rotation Normalization 102
5.3 Size Normalization 104
5.4 Illumination Normalization 106
5.5 Experimental Results and Analysis 110
5.6 Summary of This Chapter 111
Chapter 6 Extraction of Facial Expression Features 112
6.1 Introduction 112
6.2 Gabor Local Statistical Features 116
6.3 LBP Features 120
6.3.1 Basic LBP and Its Improvement 120
6.3.2 Uniform LBP Algorithm 122
6.3.3 Expression Features Extracted by LBP Histogram 124
6.4 Summary of This Chapter 125
Chapter 7 Facial Expression Recognition Based on Supervised Orthogonal Locality Preserving Projection 127
7.1 Introduction 127
7.2 LPP Algorithm 129
7.2.1 Laplacianface Algorithm 129
7.2.2 Orthogonal Laplacianface Algorithm 133
7.3 Expression Recognition with SOLPP 135
7.3.1 LDA 135
7.3.2 SOLPP 136
7.3.3 Classification of Facial Expressions 140
7.4 Experimental Results and Analysis 141
7.5 Summary of This Chapter 147
Chapter 8 Conclusion and Future Works 148
8.1 Conclusion 148
8.2 Future Works 150
References 153
Preface
Chapter 1 Introduction 1
1.1 Research Background and Significance 1
1.2 Overview of Facial Expression Recognition 4
1.3 Research Status at Home and Abroad 7
1.3.1 Typical Facial Expression Recognition Algorithms 7
1.3.2 Existing Problems in Facial Expression Recognition 13
1.4 Research Contents 14
Chapter 2 Color Constancy Algorithm for Color Image Under Complex Illumination Conditions 20
2.1 Introduction 20
2.2 Correction of Color Deviation 21
2.2.1 LoG Chrominance Edge Extraction 23
2.2.2 White Balance Adjustment 28
2.2.3 Experimental Results and Analysis 30
2.3 Luminance Enhancement 32
2.3.1 Retinex Enhancement Algorithm 32
2.3.2 Color Retention Enhancement Algorithm 35
2.3.3 Experimental Results and Analysis 40
2.4 Fractional Step Color Constancy Algorithm 42
2.5 Summary of This Chapter 45
Chapter 3 Face Detection Algorithm with Multi-feature Fusion 46
3.1 Introduction 46
3.2 Skin Color Area Detection 49
3.2.1 Common Color Space 49
3.2.2 Skin Color Clustering Analysis 55
3.2.3 Skin Color Area Detection Algorithm 57
3.3 AdaBoost Face Detection Combined with Skin Color Features 60
3.3.1 AdaBoost Algorithm 62
3.3.2 AdaBoost Face Detection Algorithm Combined with Skin Color Features 67
3.4 Face Authentication Based on Harris Corner Point Detection 69
3.4.1 Principles for Harris Corner Point Detection 69
3.4.2 Harris Corner Point Detection Algorithm 71
3.4.3 Face Authentication Based on Harris Corner Point Detection 73
3.5 Experimental Results and Analysis 74
3.6 Summary of This Chapter 76
Chapter 4 Facial Features Location 78
4.1 Introduction 78
4.2 Eye Location Based on the Robustness of Illuminated Expression 79
4.2.1 Multi-scale Self-quotient Image 80
4.2.2 Rough Location of Morphological Filtering 81
4.2.3 Precise Eye Location 85
4.2.4 Experimental Results and Analysis 86
4.3 Location System of Key Color Facial Feature Point 90
4.3.1 Overview of the System 90
4.3.2 Eyes Location 92
4.3.3 Mouth Location 95
4.3.4 Experimental Results and Analysis 97
4.4 Summary of This Chapter 99
Chapter 5 Normalization of Facial Expression Image 101
5.1 Introduction 101
5.2 Rotation Normalization 102
5.3 Size Normalization 104
5.4 Illumination Normalization 106
5.5 Experimental Results and Analysis 110
5.6 Summary of This Chapter 111
Chapter 6 Extraction of Facial Expression Features 112
6.1 Introduction 112
6.2 Gabor Local Statistical Features 116
6.3 LBP Features 120
6.3.1 Basic LBP and Its Improvement 120
6.3.2 Uniform LBP Algorithm 122
6.3.3 Expression Features Extracted by LBP Histogram 124
6.4 Summary of This Chapter 125
Chapter 7 Facial Expression Recognition Based on Supervised Orthogonal Locality Preserving Projection 127
7.1 Introduction 127
7.2 LPP Algorithm 129
7.2.1 Laplacianface Algorithm 129
7.2.2 Orthogonal Laplacianface Algorithm 133
7.3 Expression Recognition with SOLPP 135
7.3.1 LDA 135
7.3.2 SOLPP 136
7.3.3 Classification of Facial Expressions 140
7.4 Experimental Results and Analysis 141
7.5 Summary of This Chapter 147
Chapter 8 Conclusion and Future Works 148
8.1 Conclusion 148
8.2 Future Works 150
References 153
展开全部
面向人机交互的人脸表情识别技术(英文版) 节选
Chapter 1 Introduction 1.1 Research Background and Significance In social life， emotion plays an important role in coordinating the relation- ship between people.The research of emotion is more and more concerned by the society. Facial expression is a unique form for human beings to convey their intentions and emotional states. In interpersonal communica-tion， facial expression is a very important way of communication besides voice. Psychological studies indicate that whether a listener likes your speech in human communication， 7% depends on your vocabulary，38% depends on your voice， and the remaining 55% depends on your facial expressions[1]. Facial expressions， as the carrier of information， reflect much human behavior and convey the hidden information that the voice cannot do[2-3]. Facial expressions are not only a natural way for people to express emotions， but also the main symbol for people to identify emotions， which plays a very important role in affective computing. For human-computer interaction to achieve natural and harmonious communication， it is neces- sary for computers to acquire and analyze facial expression information. Therefore， the research on facial expression recognition method needs to be further developed. The computer vision technology is noninvasive， passive， cheap， and natural， so it naturally becomes the key technology in the process of facial expression recognition. While the in-depth research on such technologies as computer vision， image analysis and pattern recognition can contribute to the advancement of facial expression recognition methods. At the same time， the study of facial expression recognition incorporates many other disciplines such as pattern recognition，image processing，analysis and understanding， computer graphics， cognitive science， neural computing， physiology， psychology， and so on. Therefore， the research on this issue could greatly enhance the development of the above-mentioned disciplines. What’s more， the technology of facial expression recognition is also the core subject of the research of the intelligent human-computer interface that essentially is to train the machine to capture the changes of human emotions. With the advances in science， the computer after being trained can to a certain extent mimic the human behavior， but still does not have the human feelings. However， with the development of the facial expression recogni- tion technology， the interface can be more natural to a certain extent so that the computer can respond to humans in accordance with the changes of human emotion. From this point， it can change people’s way of life to some extent. In other words， the observation of the facial expression can be used to understand psychological states. This is， as the saying goes， to take the cue from a person’s words and facial expressions. Nevertheless， the naked eyes cannot find some very fine changes of facial expressions， or it is easily to draw an opposite conclusion from that fine changes. After the introduc- tion of machine recognition of facial expressions， the results can be obtained objectively and in real time. Therefore， we can expect facial expression recognition to replace lie detector in the security sector in the future. In terms of medical care， for some mental patients， the expression analysis can be used as an auxiliary treatment. The analysis of patient’s expression can help doctors analyze the patient’s mental state， predict what would happen when the patient is in the face of diverse outside stimuli and thus take some responding precautionary measures， so that many incidences of accidents can be avoided. Facial expressions can also be used to supple-ment the lip language to help those having hearing problem communicate with the world they live in. On the commercial side， the devices that can keep track of user’s facial expressions will certainly come into being in the near future. The applica- tion of such technology in videophone or teleconferencing is undoubtedly of great significance for strengthening domestic and international business cooperation. If the technology is applied to the automotive， the machine vision equipment can detect the driver’s fatigued state and then alert the drive. This equipment can analyze the real-time mental state of the driver and judge whether the driver’s fatigue will exert negative effect on the driver or the fatigue will make the driver sleep while driving， it then will take the necessary measures such as alarming or playing exciting music to prevent potential traffic accidents. In addition， if the existing recreational machines that have no emotions can recognize the human emotions， and adjust the strategy in accordance with the users’emotional changes， it can provide individualized services and greatly enhance the user’s entertainment interest. Moreover， an intelligent toy with personality and the ability to understand and express emotions (such as an electr
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