The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scatter matrix S,? in Linear Discriminant Analysis (LDA). Dijjrent methods have been proposed la

solve this problem in face recognition literature. Some methods reduce the dimension of the original sample space and hence unavoidably remove the null space of which has been demonstrated to contain considerable

discriminative information whereas other methods suffer from the computational problem. In this paper, we propose a new method to make use of the null space of s,eflectively and solve the small sample size problem of LDA. We compare our method with several well-known methods. and demonstrate the eficiency of our method-The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scatter matrix S,? in Linear Discriminant Analysis (LDA). Dijjrent methods have been proposed la

solve this problem in face recognition literature. Some methods reduce the dimension of the original sample space and hence unavoidably remove the null space of which has been demonstrated to contain considerable

discriminative information　whereas other methods suffer from the computational problem. In this paper, we propose a new method to make use of the null space of s,eflectively and solve the small sample size problem of LDA. We compare our method with several well-known methods. and demonstrate the eficiency of our method