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.
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Li, X., Wang, D., Chen, Y. (2013). Gait Recognition Based on Partitioned Weighting Gait Energy Image. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_13
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DOI: https://doi.org/10.1007/978-3-642-36669-7_13
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