Two-Dimensional Uncorrelated Linear Discriminant Analysis for Facial Expression Recognition
The uncorrelated discriminant linear analysis (ULDA) has been proved to be an effective feature extraction method and is known as a development of classical linear discriminant analysis (LDA). In real-world applications, we often encounter the “small sample size (SSS) problem that the number of training samples is less than the dimension of feature vectors. Under this situation, the within-class scatter matrix is always singular, making the direct implementation of the ULDA algorithm inapplicable. To tackle this problem, it is common to apply a preprocessing step that transforms the data to a lower dimensional space with loss of valuable information contains in original data. In this paper, a new technique called twodimensional uncorrelated linear discriminant analysis (2D-ULDA) is developed for solving the SSS problem. The main ingredient is the small size of covariance matrix which is suitable for the SSS problem. To evaluate the performance of the proposed 2D-ULDA, a series of experiments were performed on JAFFE database. The recognition accuracy across all experiments was higher using 2DULDA than ULDA. The comparison experiments of the proposed 2D-ULDA, 2DPCA and 2DFLD also demonstrated the competitiveness of our approach.
Uncorrelated Discriminant Analysis Uncorrelated space Optimal projection vectors Feature extraction Facial expression recognition
Wei Li Qiuqi Ruan Jun Wan
Institute of Information Science Beijing Jiaotong University Beijing, P.R.China
国际会议
2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)
北京
英文
1362-1365
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）