Regularized Neighborhood Boundary Discriminant Analysis for Facial Expression Recognition

被引：0
|
作者：
Wang, Zhan [1 ,2 ]
Ruan, Qiuqi [2 ]
Liu, Shuai [1 ,2 ]
Guo, Song [1 ,2 ]
机构：
[1] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
[2] Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China
来源：
2011 IET 4TH INTERNATIONAL CONFERENCE ON WIRELESS, MOBILE & MULTIMEDIA NETWORKS (ICWMMN 2011) | 2011年
关键词：
Feature extraction; Facial expression recognition; Fisher discriminant analysis; Linear boundary discriminant analysis; EIGENFACES;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we propose an approach which named regularized neighborhood boundary discriminant analysis for facial expression recognition. Our algorithm is based on the linear boundary discriminant analysis (LBDA), which aims to find a optimal projection in order to enhance the ability of classification. A regularized method was executed to remove the singularity of within-class metric matrix. Experiments on JAFEE facial expression database and Cohn-Kanade database show that our proposed method can get better performance than some other methods, such as linear discriminant analysis (LDA), local fisher discriminant analysis (LFDA) and linear boundary discriminant analysis (LBDA).
引用
收藏
页码：248 / 252
页数：5
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