The classical linear discriminant analysis (LDA) was previously modified by orthogonal projection into null space LDA (N_LDA) and direct LDA (D_LDA) for solving small sample size (SSS) problem. In this paper, the author proposes an extension of LDA by oblique projection, wherein N_LDA and D_LDA are included as special cases, to reduce discriminative information loss resulted from single N_LDA or D_LDA. The effectiveness of the proposed algorithm is tested by image forensics and face recognition.