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

• Published in: IEEE transactions on information forensics and security Vol. 15; pp. 3430 - 3442
• Main Authors: Limcharoen, Piya, Khamsemanan, Nirattaya, Nattee, Cholwich
• Format: Journal Article
• Language: English
• Published: New York IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; biometrics; Convolution; convolutional neural network (CNN); Convolutional neural networks; Feature extraction; Gait recognition; Human identification; joint replacement coordinates (JRCs); Joints (anatomy); k-nearest neighbor (KNN); kinect; Legged locomotion; multilayer perceptron (MLP); Neural networks; Shape; Skeleton; view independent
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2020.2985535

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.