Gait recognition based on partitioned weighting gait energy image

Authors:
Xiaoxiang Li;Dimin Wang;Youbin Chen
Affiliations:
Graduate School at Shenzhen, Tsinghua University, China;Graduate School at Shenzhen, Tsinghua University, China;Graduate School at Shenzhen, Tsinghua University, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

Gait energy image (GEI) has been proved to be an effective gait feature representation method. But it's sensitive to the change of clothing and carrying conditions. We propose a novel gait recognition method called partitioned weighting gait energy image (PWGEI) to deal with these problems. A human body is divided into four parts and different weights are given to different parts to get the PWGEI from GEI. Two different weighting ways are conducted and a fusion of classifiers is adopted. We test our method on the USF database. Our average recognition rate is 48.87%, which is higher than GEI by 6% and higher than gait flow image (GFI) by 5.79%. The experimental results prove the effectiveness of our proposed PWGEI method.