Gait energy image (GEI) is composed of static body silhouette and dynamic frequency information of human gait. To achieve fast and efficient gait recognition, combined with the accurate description of the information of details and directions in image by Curvelet transform, a gait recognition method using GEI and Curvelet (GEIC) is presented. Firstly, to gain the gait energy images, the gait cycle is selected according to the aspect ratio. Secondly, Curvelet energy coefficients of the GEI, which are used as gait feature vector, are extracted by Curvelet transform in different scales and different directions. Finally, the gait recognition is accomplished by the K nearest neighbor (KNN) classifier. The experimental results demonstrate that GEIC performs well on CASIA(B) database, with the average accuracy of 86.83%. Compared with GEI+KPCA, GEI+W(2D)2PCA and GEI+(2D)2PCA, the algorithm GEIC achieves better robustness in the condition of the person wearing or packaging.
Gait recognition using GEI and curvelet
First published at:Apr 15, 2017
Opto-Electronic Engineering Vol. 44, Issue 04, pp. 400 - 404 (2017) DOI:10.3969/j.issn.1003-501X.2017.04.003
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Get Citation: Luo Jing, Zi Chunyuan, Zhang Jianliang, et al. Gait recognition using GEI and curvelet[J]. Opto-Electronic Engineering, 2017, 44(4): 400–404.