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In this paper we present a feature representation method based on Kernel Independent Component Analysis for gait recognition. The Kernel ICA combines the strengths of both Kernel and Independent Component Analysis (ICA) approaches. Principal Component Analysis (PCA) is performed as a preparation for Kernel ICA, and then we use Kernel ICA algorithm to obtain the Independent Components (IC). The mean IC coefficients are used to represent different gaits. We compare the performance of Kernel ICAdoi:10.1109/dicta.2007.4426849 dblp:conf/dicta/WangLHZ07 fatcat:447hbtb66ncxbaacsavljfrw3a