Human gait recognition using Dual-Tree Complex Wavelet Transform

Abstract

In this paper, the performance of gait recognition is improved by developing a new gait feature template. Dual-Tree Complex Wavelet Transform (DT-CWT) is used for feature template generation. DT-CWT is applied to gait images in an arbitrary decomposition level and the magnitude of the resulting six band-pass sub-images is computed. The feature template is generated by concatenating these sub-images into a single image. Furthermore, to avoid overlearning, a classifier ensemble method called Random Subspace Method (RSM) is used for classification. Experimental results on the USF database demonstrate the efficacy of the proposed model.