<FONT face=Verdana>Orthogonal discriminant locality preserving projections is an effective feature extraction

but it may encounter the small size samples problem when it is applied in face recognition task. In addition

it is only a linear feature extraction technique. A kernel orthogonal discriminant locality preserving projections is proposed. The key is to how to compute the nonzero space of the total scatter matrix in the higher dimensional feature space. As to this problem

the kernel function technique and the eigenfaces method that transforms the computation of the high order matrix into the computation of the low order matrix are used

and then the actual computation of the nonzero space of the total scatter matrix in the higher dimensional feature space is reduced to a standard eignenvalue problem. In addition

the proposed algorithm can effectively overcome small size samples problem. The numerical experiments on facial databases show that the proposed method is effective and feasible.