Gait recognition using GEI and curvelet

Authors

Jing Luo, Tianjin Polytechnic University, University of Wollongong, Key Laboratory Of Advanced Electrical Engineering And Energy Technology
Chunyuan Zi, Key Laboratory Of Advanced Electrical Engineering And Energy Technology, Tianjin Polytechnic University
Jianliang Zhang, Tianjin Polytechnic University
Yue Liu, Tianjin Polytechnic University

RIS ID

115892

Publication Details

J. Luo, C. Zi, J. Zhang & Y. Liu, "Gait recognition using GEI and curvelet," Guangdian Gongcheng/Opto-Electronic Engineering, vol. 44, (4) pp. 400-404, 2017.

Abstract

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) 2 PCA, the algorithm GEIC achieves better robustness in the condition of the person wearing or packaging.