Abstract：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.
李欣，王科俊，贲晛烨. 基于MW(2D)PCA的单训练样本人脸识别[J]. 模式识别与人工智能, 2010, 23(1): 77-83.
LI Xin,WANG Ke-Jun,BEN Xian-Ye. MW(2D)2PCA Based Face Recognition with Single Training Sample. , 2010, 23(1): 77-83.
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2010, Vol. 23 
