Algorithm Study of Face Recognition on Improved 2DLDA

Abstract:

Article Preview

Linear Discriminant Analysis (LDA) [1] is a well-known method for face recognition in feature extraction and dimension reduction. To solve the “small sample” effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) [2] has been used for face recognition recently,but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method (N2DLDA) outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

58-61

DOI:

10.4028/www.scientific.net/AMM.128-129.58

Citation:

S. P. Li et al., "Algorithm Study of Face Recognition on Improved 2DLDA ", Applied Mechanics and Materials, Vols. 128-129, pp. 58-61, 2012

Online since:

October 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.