[1] Zhou Xiaoyan, Zheng Wenming, Zou Cairong, et al. Facial expression recognition based on fuzzy-LDA/CCA [J]. Journal of Southeast University (English Edition), 2008, 24 (4): 428-432. [doi:10.3969/j.issn.1003-7985.2008.04.007]

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Facial expression recognition based on fuzzy-LDA/CCA()

基于模糊LDA/CCA的面部表情识别

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

24

Issue:

2008 4

Page:

428-432

Research Field:

Computer Science and Engineering

Publishing date:

2008-12-30

Info

Title:

Facial expression recognition based on fuzzy-LDA/CCA

基于模糊LDA/CCA的面部表情识别

Author(s):

Zhou Xiaoyan¹;  2;  Zheng Wenming³;  Zou Cairong¹;  4;  Zhao Li¹

¹School of Information Science and Engineering, Southeast University, Nanjing 210096, China
²School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
³Research Center for Learning Science, Southeast University, Nanjing 210096, China
⁴ Foshan University, Foshan 528000, China

周晓彦¹;  2;  郑文明³;  邹采荣¹;  4;  赵力¹

¹东南大学信息科学与工程学院, 南京 210096; ²南京信息工程大学电子与信息工程学院, 南京 210044; ³东南大学学习科学中心, 南京 210096; ⁴佛山科技学院, 佛山 528000

Keywords:

fuzzy linear discriminant analysis;  canonical correlation analysis;  facial expression recognition

模糊判别分析;  典型相关分析;  面部表情识别

PACS:

TP391.41

DOI:

10.3969/j.issn.1003-7985.2008.04.007

Abstract:

A novel fuzzy linear discriminant analysis method by the canonical correlation analysis(fuzzy-LDA/CCA)is presented and applied to the facial expression recognition.The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs.CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship.Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method.The performance of the proposed method is demonstrated using real data.

提出了一种新颖的基于典型相关分析(CCA)的模糊判别分析方法(fuzzy-LDA/CCA), 并应用于面部表情识别问题.首先为每幅表情图像建立一个相关联的类模糊隶属度矢量, 用于表示表情图像与基本表情类别的隶属关系, 在此基础上应用CCA方法建立表情图像同表情类别的关系表达式, 最后通过对表情图像的类隶属度矢量的估计来实现表情的分类.此外, 还将fuzzy-LDA/CCA方法在核空间中进行了非线性推广, 从而来解决非线性判别分析的问题.实验证明提出的方法获得了更好的识别效果.

References:

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[3] Belhumeur P N, Hespanha J P, Kriegman D J.Eigenfaces vs.fisherfaces:recognition using class specific linear projection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7):711-720.
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[10] Wu Qingjiang, Zhou Xiaoyan, Zheng Wenming.Facial expression recognition using fuzzy kernel discriminant analysis[C]//Lecture Notes in Computer Science:Fuzzy System and Knowledge Discovery. Berlin-Heidelberg:Springer-Verlag, 2006:780-783.
[11] Zheng Wenming, Zhou Xiaoyan, Zou Cairong, et al.Facial expression recognition using kernel canonical correlation analysis(KCCA)[J].IEEE Transactions on Neural Networks, 2006, 17(1):233-238.
[12] Lyons M, Budynek J, Akamatsu S.Automatic classification of single facial images[J].IEEE Trans on Pattern Analysis and Machine Intelligence, 1999, 21(12):1357-1362.
[13] Zhang Z, Lyons M, Schuster M, et al.Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perception[C]//Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition.Nara, Japan, 1998:454-459.
[14] Ekman P, Friesen W V.Pictures of facial affect [R].San Francisco:Human Interaction Laboratory of University of California Medical Center, 1976.

Memo

Memo:

Biographies: Zhou Xiaoyan(1980—), female, graduate;Zheng Wenming(corresponding author), male, doctor, professor, wenming_-zheng@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.60503023, 60872160), the Natural Science Foundation for Universities of Jiangsu Province(No.08KJD520009), the Intramural Research Foundation of Nanjing University of Information Science and Technology(No.Y603).
Citation: Zhou Xiaoyan, Zheng Wenming, Zou Cairong, et al.Facial expression recognition based on fuzzy-LDA/CCA[J].Journal of Southeast University(English Edition), 2008, 24(4):428-432.

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