Title :
Gait recognition based on KDA and SVM
Author :
Qi Yang ; Yali Tian
Author_Institution :
Sch. of Mech. Eng., Shenyang Ligong Univ., Shenyang, China
Abstract :
In this paper, we used gait silhouettes that provided by CASIA, and all we study are based on this database. Firstly, we normalized and centralized gait silhouettes and get the gait sequence, secondly, we extract the active regions by calculating the difference of two adjacent silhouettes images, and construct an AEI by accumulating these active regions, finally, using Kernel Discriminant Analysis (KDA) method to analysis the AEI, and parameter optimization method used to determine the nuclear function of KDA, and using SVM to classified and recognized gait. Experimental results show that such methods to be identified effective.
Keywords :
feature extraction; image recognition; optimisation; support vector machines; AEI; CASIA; KDA; SVM; active energy image; gait recognition; gait sequence; gait silhouette; kernel discriminant analysis; nuclear function; parameter optimization method; Accuracy; Covariance matrix; Equations; Feature extraction; Kernel; Mathematical model; Support vector machines; AEI; Eigenvector; FDEI; KDA; SVM;
Conference_Titel :
Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-8622-9
DOI :
10.1109/ITAIC.2011.6030175
