Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
