Null Space-Based Lda With Weighted Dual Personal Subspaces For Face Recognition

Linear discriminant analysis (LDA) is popular feature extraction technique for face recognition. However, it often suffers the small sample size problem when dealing with the high dimensional face data. Moreover, the within-class and between-class scatter matrix used in LDA have low effective when dealing with face data of non-gaussian density. In this paper, we propose a new method for face recognition. We first calculate the weighted dual personal subspaces to replace the within and between class matrix, then null space-based LDA is performed. The experiments show our method outperforms existing LDA and state-of-art face recognition approaches