View-Independent Gait Recognition Using Joint Replacement Coordinates (JRCs) and Convolutional Neural Network

Abstract

Gait recognition has received increasing attention for security and authentication since it can be done unintrusively from afar and without a subject’s awareness. In this work, we propose a new model-based gait recognition technique called JRC-CNN gait recognition. We introduce three new concepts. (1) We create a new way to preprocess skeleton data by rotating skeleton data using two virtual axes. This process reduces the fluctuation in movements and resolves the multi-viewpoint issue. All postures in a walk are observed from the same angle. (2) We introduce new Joint Replacement Coordinates (JRCs), which represent the movements of the left and right joints in a group of three connected joints. These JRC gait features are designed to put more emphasis on local movements than the movements of non-connected joints. (3) We construct a new Convolution Neural Network (CNN) for the classification process, which consists of a convolutional layer on each JRC and two fully-connected layers. A convolutional layer is designed to discover relations within a group of three connected joints. Fully-connected layers also find the relations of all groups of three connected joints throughout an entire body (in a posture). Our JRC-CNN technique achieves above 98.4% accuracy and significantly outperforms other existing techniques for all free-direction walk datasets. It also performs well under the gallery-size test and the CMC curve test. This means that our proposed JRC-CNN gait recognition technique can be used in a real-world situation. Experimental results also suggest that a person can be identified by a unique posture (an entire body is observed as a whole) with the focus on the movements of connected joints.