Abstract
Gait recognition has recently attracted increasing interest from the biometric society. In this paper, we present a gait recognition system based on the fusion of multiple gait cycles using a new gait representation. First, a gait sequence is automatically partitioned into multiple gait cycles by finding the local minima of width signal. After gait cycle partitioning, we extract a new gait feature called motion contour image (MCI) that captures the contour of the binary silhouette image of a walking individual. Finally, for human identification, the outputs of nearest neighbor classifiers are fused at a decision level based on majority voting. Our proposed system is tested on the CASIA gait dataset A. Experimental results show that the proposed system is better than or equal to previous works in terms of correct classification rate.
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.
