Share Email Print

Proceedings Paper

Zernike moments features for shape-based gait recognition
Author(s): Huanfeng Qin; Lan Qin; Jun Liu; Jiang Chao
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 15 November 2011
PDF: 6 pages
Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 832132 (15 November 2011); doi: 10.1117/12.905211
Show Author Affiliations
Huanfeng Qin, Chongqing Univ. (China)
Lan Qin, Chongqing Univ. (China)
Jun Liu, Chongqing Univ. (China)
Jiang Chao, Chongqing Univ. (China)

Published in SPIE Proceedings Vol. 8321:
Seventh International Symposium on Precision Engineering Measurements and Instrumentation
Kuang-Chao Fan; Man Song; Rong-Sheng Lu, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?