MW(2D)~2PCA Based Face Recognition with Single Training Sample
Ke Wang · 2010
Traditional methods get low recognition accuracy in the condition of only one training sample,therefore,it is a great challenge for face recognition. In this paper,a two-directional two-dimensional principal component analysis((2D)2PCA) is developed to solve this problem. An improved arithmetic combining weight and block called modular weighted (2D)2PCA is proposed for efficient local feature extraction. Besides,the fuzzy theory is introduced to classify the single sample face recognition. Experimental results on ORL and a subset of CAS-PEAL face databases show that the presented method achieves a high recognition accuracy.