Facial Expression Recognition Based on Two Dimensional Feature Extraction

摘要：

In this paper,approaches to facial expression recognition based on two dimension methods are studied.2D expression feature extraction methods include 2DPCA,2DLDA and Generalized Low Rank Approximation of Matrices (GLRAM) which are usually used in face recognition or data compression.After expression features are extracted,support vector machine classifier is used for expression classification.Extensive expression recognition experiments are carried out on Japanese Female Facial Expression database (JAFFE) to study the influence of feature dimension on recognition ratio and the results are also compared with that of the tradition 1D feature extraction methods such as PCA and LDA.Experiment results show that 2D methods are effective in expression recognition application and usually outperform 1D methods.

作者: Ying Zilu Li Jingwen Zhang Youwei

作者单位: School of Electronics and Information Engineering,Beihang University,Beijing 100083;School of Inform School of Electronics and Information Engineering,Beihang University,Beijing 100083

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）