Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model

Abstract

Gait recognition is a relatively new subfield in biometric recognition, which attempts to recognize people from the way they walk or run. This paper discusses silhouette-based feature descriptor. Human silhouette geometry is generated by boundary tracking approach and resampled to a normalized format. Boundary-centroid distance is proposed to describe gait modality. Then, we apply wavelet transform to boundary-centroid distance, and extract wavelet descriptor. At the same time, we obtain the human skeleton model and extract bodys dynamic parameters to express gait modality. We carry out human identification based on SVM using the two kinds of gait feature. The performances based on the two features are compared. Multiple feature fusion and multiple views fusion are carried out and the recognition results demonstrate that the performance of multiple features and multiple views recognition is better than any single feature and single view recognition.