Face recognition method based on fuzzy 2DPCA

2DPCA, which is one of the most important face recognition methods, is relatively sensitive to substantial variations in light direction, face pose, facial expression. In order to improve the recognition performance of traditional 2DPCA, a new 2DPCA algorithm based on the fuzzy set theory is proposed in this paper, namely, the fuzzy 2DPCA(F2DPCA). In this method, applying fuzzy K-nearest neighbor (FKNN), the membership degree matrix of training samples is calculated, which is used to get fuzzy means of each class. The average of fuzzy means is then incorporate into the definition of general scatter matrix with anticipation that such algorithm helps improve classification result. The comprehensive experiments on the ORL, YALE and FERET face databases show that the proposed method can improved classification rates and reduced sensitivity to variations between face images caused by changes in illumination, face expression, face pose.