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.
王科俊，贲晛烨，刘丽丽，李雪峰. 基于子模式的完全二维主成分分析的步态识别算法*[J]. 模式识别与人工智能, 2009, 22(6): 854-861.
WANG Ke-Jun, BEN Xian-Ye, LIU Li-Li, LI Xue-Feng. Subpattern-Based Complete Two Dimensional Principal Component Analysis for Gait Recognition. , 2009, 22(6): 854-861.
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2009, Vol. 22 
