Zernike moments features for shape-based gait recognition

Huanfeng Qin; Lan Qin; Jun Liu; Jiang Chao

doi:10.1117/12.905211

15 November 2011 Zernike moments features for shape-based gait recognition

Huanfeng Qin, Lan Qin, Jun Liu, Jiang Chao

Proceedings Volume 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation; 832132 (2011) https://doi.org/10.1117/12.905211
Event: Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 2011, Yunnan, China

Abstract

The paper proposes a new spatio-temporal gait representation, called cycles gait Zernike moments (CGZM), to characterize human walking properties for individual recognition. Firstly, Zernike moments as shape descriptors are used to characterize gait silhouette shape. Secondly, we generate CGZM from Zernike moments of silhouette sequences. Finally, the phase and magnitude coefficientsof CGZM are utilized to perform classification by the modified Hausdorff distance (MHD) classifier. Experimental results show that the proposed approach have an encouraging recognition performance.

Citation Download Citation

Huanfeng Qin, Lan Qin, Jun Liu, and Jiang Chao "Zernike moments features for shape-based gait recognition", Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 832132 (15 November 2011); https://doi.org/10.1117/12.905211

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