Modular PCA Face Recognition Based on Weighted Average

This paper presents an improved modular PCA approach, that is, modular PCA algorithm based on weighted average. This algorithm extracts weighted average for every sub-block of every training sample in each type of training sample, and normally operates the corresponding sub-block in training sample using weighted average, then all standardized sub-blocks constitute the overall scatter matrix, and thus the optimal projective matrix is obtained; From the middle value of sub-blocks in training set, and normally projecting sub-blocks of training samples and test samples to the projective matrix, then we can get identified characteristics; At last, use the recent distance classifier to class. The test results in the ORL face database show that the proposed method in identifying performance is superior to ordinary modular PCA approach.