Gait recognition based on KDA and SVM

Author

Qi Yang ; Yali Tian

Author_Institution

Sch. of Mech. Eng., Shenyang Ligong Univ., Shenyang, China

Volume

1

fYear

2011

fDate

20-22 Aug. 2011

Firstpage

160

Lastpage

163

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International

Conference_Location

Chongqing

Print_ISBN

978-1-4244-8622-9

Type

conf

DOI

10.1109/ITAIC.2011.6030175

Filename

6030175