Discriminative Common Images for Face Recognition

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Basically, in LDA the image always needs to be transformed into 1D vector, however recently two-dimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images without transforming into 1D vector. In this paper, we propose a new LDA-based method that applies the idea of two-dimensional PCA. In addition to that, our approach proposes an method called Discriminative Common Images based on a variation of Fisher’s LDA for face recognition. Experiment results show our method achieves better performance in comparison with the other traditional LDA methods.