Deep Learning Approaches for Human Gait Recognition: A Review

Abstract

Many biometric authentication techniques have been defined over the years; of these techniques, Human Gait recognition has gathered popularity over the years due to its ability to recognize a person from a distance. As the data has grown in size the focus has shifted from basic Machine Learning algorithms to Deep Learning based approaches. This paper aims to review the various deep-learning approaches used in the discipline of gait identification. This review comprises recent trends in these deep learning approaches, Convolutional Neural networks, Capsule Networks, Recurrent Neural Networks, Autoencoders, Deep Belief Networks, and Generative Adversarial Networks.