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The Modular-2DPCA is an improvement and promotion of 2DPCA. Modular-2DPCA method creates the covariance matrix by blocked sub image, which make better robustness. There are many parts in a human face, with each part could own different weight in face recognition, block made the research of parts became possible. This paper directs the characteristic of block, calculating the mean value and covariance matrix for each sub image block, with which can extract the features of each part of human face more accurate. Theoretically, this method can efficacious reduce the effect of changed facial. Otherwise, this paper contains a preliminary research of sub image weights setting. Weights of parts can further raise contribution of some special parts of human face. Appropriate weights can improve the result of recognition. Experiments show that this method can efficacious improves the insignificancy of Modular 2DPCA in features extracting and raise the correct result of recognition.