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 d...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.展开更多
基金The Graduate Education Steering Committee of National Engineering Professional Degree (2016-ZX-064)Natural Science Foundation of Tianjin of China (16JCYBJC 15400)
文摘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.
