Algorithm Study of Face Recognition on Improved 2DLDA

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 128-129)
• Pages: 58-61
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.128-129.58
• Online since: October 2011
• Authors: Shi Ping Li, Yu Cheng, Hui Bin Liu, Lin Mu
• Keywords: 2DLDA, Face Recognition, Feature Extraction, Scatter Matrix

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