Human Gait Recognition Based on Kernel Independent Component Analysis

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 ICA with some classical algorithms such as FastICA and Baseline etc. within the context of appearance- based gait recognition problem using the CMU MoBo database and USF Challenge database. Experimental results show that Kernel ICA based method gives a competitive performance in both accuracy and convergence speed in gait recognition problem.