Abstract A gait recognition method based on subpattern complete two dimensional principal component analysis (SpC2DPCA) is proposed. Firstly, gait energy images are divided into small sub-images and any ineffectual subblock is removed adaptively. Then, C2DPCA approach is applied to every sub-image directly to obtain sub-feature. Finally, those sub-features are synthesized into the whole for subsequent classification using the nearest neighbor classifier. The proposed gait recognition method is evaluated on the CASIA gait database, and the number of sub-pattern division is determined through experiments. The experimental results demonstrate that the performance of SpC2DPCA is obviously superior to that of C2DPCA.The proposed method is effective in local feature extraction and person identification with clothes changing, backpacking and direction of gait changing.
WANG Ke-Jun,BEN Xian-Ye,LIU Li-Li等. Subpattern-Based Complete Two Dimensional Principal Component Analysis for Gait Recognition[J]. , 2009, 22(6): 854-861.
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2009, Vol. 22 
