A Novel Method Of Facial Expression Recognition Based On GPLVM Plus SVM

摘要：

The dimensionality reduction has always been a long lasting thorny problem on the study of facial expression recognition (FER). In this paper, we propose a novel method of facial expression recognition, which using Gaussian process latent variable models (GPLVM) for reducing the high dimensional data of facial expression images into a relatively low dimension data and using support vector machine (SVM) classifier for the expression classification lately. By applying this algorithm to Japanese Female Facial Expression (JAFFE) database for facial expression recognition, we find that the proposed new algorithm has a better performance than the traditional algorithms, such as PCA and LDA etc.. This have further proved the effectiveness of our proposed algorithm.

关键词： Facial expression recognition(FER) GPLVM SVM PCA LDA

作者: Huang, M.W. Wang, Z.W. Ying, Z.L.

作者单位: School of Information Engineering, WUYI University, Jiangmen, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 916-919

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）