Title: A combined classifier kNN-SVM in gait-based biometric authentication system

Authors: L.R. Sudha; R. Bhavani

Addresses: Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu 608002, India ' Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu 608002, India

Abstract: The objective of this paper is to develop an efficient authentication system with reduced search space to recognise individuals based on their gait when they enter into surveillance area. To achieve this objective: 1) we have split the database into two based on gender and then the search is restricted to the identified gender database; 2) based on one gaitcycle, we have selected gait representing static and dynamic features which are invariant to various covariate factors such as wearing coats and carrying; 3) we used the decisions of both k-nearest neighbour (kNN) and support vector machine (SVM) by decision level fusion. Experimental results evaluated on the benchmark CASIA B gait dataset shows superior performance in terms of correct classification rate and it shows robustness to variations in clothing and carrying conditions.

Keywords: biometric authentication; biometrics; gait recognition; silhouette images; spatio-temporal; kNN classifiers; k-nearest neighbour; support vector machine; SVM; video surveillance; gender; classification rate; clothing; carrying items.

DOI: 10.1504/IJCAT.2014.060522

International Journal of Computer Applications in Technology, 2014 Vol.49 No.2, pp.113 - 121

Available online: 18 Apr 2014 *

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