A robust gait recognition system using spatiotemporal features and deep learning

Abstract

Gait recognition plays a very vital role in many practical applications of computer and robot vision in smart environments such as health care for elderly using smart home technology. Hence, it has been attracting considerable attentions from many machine vision researchers in last decades. In this paper, we propose a novel method for depth video-based gait recognition using robust features and deep learning. Local Directional Pattern (LDP) features are first extracted from depth silhouettes. Then, LDP features are augmented with optical flow motion features to generate spatiotemporal robust features. The features are then applied on a Convolutional Neural Network (CNN) for training and recognition. The proposed method outperforms the conventional gait recognition approaches. This system can contribute in various practical applications such as observing elderly peoples' gait patterns in smart homes or hospitals.