Multi-view gait recognition method based on RBF network

Abstract

Gait is an important biometrics in human identification, but the view variation problem seriously affects the accuracy of gait recognition. Existing methods for multi-view gait-based identification mainly focus on transforming the features of one view to another view, which might be unsuitable for the real applications. In this paper, we propose a multi-view gait recognition method based on RBF network that employs a unique view-invariant model. First, extracts the gait features by calculating the gait individual image (GII), which could better capture the discriminative information for cross view gait recognition. Then, constructs a joint model, use the DLDA algorithm to project the model and get a projection matrix. Finally, the projected eigenvectors are classified by RBF network. Experiments have been conducted in the CASIA-B database to prove the validity of the proposed method. Experiment results shows that our method performs better than the state-of-the-art multi-view methods.