This paper addresses two LDA problems in face recognition. The first one is small sample size (S3) problem while the second is illumination and pose variations. To overcome the S3 problem, this paper proposes a new method in subspace approach in determining the optimal projection for LDA. Also, an in-depth investigation is conducted on the influence of different illuminations and poses variations. Comparisons with existing LDA-based methods are performed using FERET and Yale Group B face databases. The experimental results show that the proposed method gives the best performance comparing with the existing LDA-based methods for both databases. Moreover, the computational cost of the proposed method is near the same as the existing fastest LDA-based method.