Effective classification image space which can solve small sample size problem

Linear discriminant analysis (LDA) is one of the most popular methods in feature extraction and dimension reduction. However, in many real applications, particularly in image recognition applications such as face recognition, conventional LDA algorithm will often encounter small sample size problem. In this paper, an effective classification image space is defined and optimal features are extracted from this space. With the proposed method, an effective classification image space of each original image is first obtained. Then, optimal features are extracted from this space. The small sample size problem is solved effectively with the proposed method. Experimental results on XM2VTS face database demonstrate the effectiveness of the proposed method