Performance of convolutional neural networks for human identification by gait recognition

Abstract

Background and Objective: Natural walk and topological analysis of human being have respective and certainly unique key features that allow identifications when other biometric techniques are not visible. The objective of this paper is to draw attention towards a simple and novel feature extractor for gait recognition that is based on a deep learning approach. Materials and Methods: Different from conventional ways and means, the gait is designated as regular and intermittent motion taken out directly from silhouettes. Before the use of convolutional neural network to learn human gait representations, two important data pre-processing stages are enforced to enhance the characteristics of gait patterns obtained from grayscale images. Results: The proposed gait recognition approach achieves impressive results in terms of training/validation accuracy and mean square errors. Conclusion: The conducted experimental outcomes report competitive performance as compared to many traditional machine learning methods and previous deep gait models specifically for the case of low-image resolutions and large-scale dataset of input images.