Appearance-Based Gait Recognition Using Independent Component Analysis
For human identification at distance (HID) applications, gait characteristics are hard to conceal and has the inherent merits such as non-contact and unobtrusive. In this paper, a novel appearance-based method for automatic gait recognition is proposed using independent component analysis (ICA). Principal component analysis (PCA) is performed on image sequences of all persons to get the uncorrelated PC coefficients. Then, ICA is performed on the PC coefficients to obtain the more independent IC coefficients. The IC coefficients from the same person are averaged and the mean coefficients are used to represent individual gait characteristics. For improving computational efficiency, a fast and robust method named InfoMax algorithm is used for calculating independent components. Gait recognition performance of the proposed method was evaluated by using CMU MoBo dataset and USF Challenge gait dataset. Experiment results show the efficiency and advantages of the proposed method.
KeywordsRecognition Rate Independent Component Analysis Independent Component Analysis Slow Walk Gait Recognition
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