Human Face Recognition Method Based on Modular PCA
In this paper, a new technique called M2PCA+FDA is developed for human face recognition. First, in proposed approach, the original sample images are divided into smaller modular images, which are also called sub-images, then, for feature extraction, the well-known PCA method can be directly used to the sub-images obtained from the previous step, and the new lower dimensionality patterns that can replace the original patterns are obtained. In the end, the classical Fisherfaces method is performed on the reductions for the pattern classification. The advantage of the represented way when compared with conventional PCA method on original images is that the local discriminant features of the original patterns can be efficiently extracted by the modular PCA, which are available to differentiate one class from another.To test M2PCA+FDA and to evaluate its performance, a series of experiments will be performed on two human face image databases: ORL and NJUST603 human face databases. The experimental results indicate that the performance of the new method is obviously superior to that of both Fisherfaces and PCA.