Multi-view Gait Based Biometric System

This paper presents a multi-view gait based biometric system that able to work well in various walking trajectories and covariate factors such as clothing, load carrying and speed of walking. Our approach first applies perspective correction to alter the silhouettes from oblique view to side-view plane. Next, joint locations of hip, knees and ankles are estimated based on a priori information of human body proportion. Dynamic and static gait features are then extracted by the proposed extraction technique. Gaussian filter is applied to smooth the features in order to reduce the influence of outliers. Feature normalization and selection are subsequently applied before the classification process. The experiments were carried out on SOTON Oblique Database and SOTON Covariate Database from University of Southampton. From the experimental results, the proposed system achieved 92.5% and 96.0% correct classification rates for both databases respectively. Keyword: gait recognition, biometrics, covariate factors * Corresponding Author: Ng Hu, Faculty of Computing and Informatics, Multimedia University, Malaysia, Email: [email protected]