Two-directional maximum scatter difference discriminant analysis for face recognition

In this paper, we propose a novel method for image feature extraction. This method combines the ideas of two-dimensional principal component analysis and two-dimensional maximum scatter difference and which can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the scatter difference criterion. The proposed method not only avoids the singularity problem frequently occurred in the classical Fisher discriminant analysis due to the small sample size, but also saves much computational time. In addition, the proposed method can simultaneously make use of the discriminant information and descriptive information of the image. Experiments conducted on FERET, and ORL face databases demonstrate the effectiveness of the proposed method.