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

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.