Gait recognition by combining wavelets and geometrical features

Biometric system provides more reliable and efficient means of identity verification. Gait recognition is the process of identifying a person by the way they walk. It is one kind of biometric technology that can be used to monitor people without their co-operation and has been receiving wide attention in the computer vision community. In this paper, we propose a new approach for extracting human gait features from a walking subject based on wavelet coefficients and geometrical features of the silhouette. The proposed system is tested on CASIA dataset. The experimentation results indicate that the proposed system works efficiently by combining geometrical features and wavelet coefficients. The proposed decision fusion enables the performance improvement by integrating multiple ones with different confidence measures.