面向人机交互的人脸表情识别技术（英文版）

　　《面向人机交互的人脸表情识别技术=Facial Expression RecognitionTechnology for Human-Computer Interaction：英文》根据工程应用的实际需要，全面系统地介绍了面向人机交互的人脸表情识别技术，主要内容包括：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，解决传统颜色恒常性的不适定问题；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；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；考虑融入表情样本的类别信息，对有监督的正交保局投影的表情识别算法进行研究。

暂缺《面向人机交互的人脸表情识别技术（英文版）》作者简介

Contents \n
Preface \n
Chapter 1　Introduction　1 \n
1.1　Research Background and Significance　1 \n
1.2　Overview of Facial Expression Recognition　4 \n
1.3　Research Status at Home and Abroad　7 \n
1.3.1　Typical Facial Expression Recognition Algorithms　7 \n
1.3.2　Existing Problems in Facial Expression Recognition　13 \n
1.4　Research Contents　14 \n
Chapter 2　Color Constancy Algorithm for Color Image Under Complex Illumination Conditions　20 \n
2.1　Introduction　20 \n
2.2　Correction of Color Deviation　21 \n
2.2.1　LoG Chrominance Edge Extraction　23 \n
2.2.2　White Balance Adjustment　28 \n
2.2.3　Experimental Results and Analysis　30 \n
2.3　Luminance Enhancement　32 \n
2.3.1　Retinex Enhancement Algorithm　32 \n
2.3.2　Color Retention Enhancement Algorithm　35 \n
2.3.3　Experimental Results and Analysis　40 \n
2.4　Fractional Step Color Constancy Algorithm　42 \n
2.5　Summary of This Chapter　45 \n
Chapter 3　Face Detection Algorithm with Multi-feature Fusion　46 \n
3.1　Introduction　46 \n
3.2　Skin Color Area Detection　49 \n
3.2.1　Common Color Space　49 \n
3.2.2　Skin Color Clustering Analysis　55 \n
3.2.3　Skin Color Area Detection Algorithm　57 \n
3.3　AdaBoost Face Detection Combined with Skin Color Features　60 \n
3.3.1　AdaBoost Algorithm　62 \n
3.3.2　AdaBoost Face Detection Algorithm Combined with Skin Color Features　67 \n
3.4　Face Authentication Based on Harris Corner Point Detection　69 \n
3.4.1　Principles for Harris Corner Point Detection　69 \n
3.4.2　Harris Corner Point Detection Algorithm　71 \n
3.4.3　Face Authentication Based on Harris Corner Point Detection　73 \n
3.5　Experimental Results and Analysis　74 \n
3.6　Summary of This Chapter　76 \n
Chapter 4　Facial Features Location　78 \n
4.1　Introduction　78 \n
4.2　Eye Location Based on the Robustness of Illuminated Expression　79 \n
4.2.1　Multi-scale Self-quotient Image　80 \n
4.2.2　Rough Location of Morphological Filtering　81 \n
4.2.3　Precise Eye Location　85 \n
4.2.4　Experimental Results and Analysis　86 \n
4.3　Location System of Key Color Facial Feature Point　90 \n
4.3.1　Overview of the System　90 \n
4.3.2　Eyes Location　92 \n
4.3.3　Mouth Location　95 \n
4.3.4　Experimental Results and Analysis　97 \n
4.4　Summary of This Chapter　99 \n
Chapter 5　Normalization of Facial Expression Image　101 \n
5.1　Introduction　101 \n
5.2　Rotation Normalization　102 \n
5.3　Size Normalization　104 \n
5.4　Illumination Normalization　106 \n
5.5　Experimental Results and Analysis　110 \n
5.6　Summary of This Chapter　111 \n
Chapter 6　Extraction of Facial Expression Features　112 \n
6.1　Introduction　112 \n
6.2　Gabor Local Statistical Features　116 \n
6.3　LBP Features　120 \n
6.3.1　Basic LBP and Its Improvement　120 \n
6.3.2　Uniform LBP Algorithm　122 \n
6.3.3　Expression Features Extracted by LBP Histogram　124 \n
6.4　Summary of This Chapter　125 \n
Chapter 7　Facial Expression Recognition Based on Supervised Orthogonal Locality Preserving Projection　127 \n
7.1　Introduction　127 \n
7.2　LPP Algorithm　129 \n
7.2.1　Laplacianface Algorithm　129 \n
7.2.2　Orthogonal Laplacianface Algorithm　133 \n
7.3　Expression Recognition with SOLPP　135 \n
7.3.1　LDA　135 \n
7.3.2　SOLPP　136 \n
7.3.3　Classification of Facial Expressions　140 \n
7.4　Experimental Results and Analysis　141 \n
7.5　Summary of This Chapter　147 \n
Chapter 8　Conclusion and Future Works　148 \n
8.1　Conclusion　148 \n
8.2　Future Works　150 \n
References　153