Gait Recognition Based on Deep Learning: A Survey

TL;DR

This survey reviews recent deep learning methods for gait recognition, highlighting their advantages, limitations, and the datasets and architectures used to improve biometric identification accuracy.

Contribution

It provides a comprehensive overview of deep learning-based gait recognition techniques, categorizing approaches, datasets, and architectures, and discusses their strengths and weaknesses.

Findings

Deep learning significantly improves gait recognition accuracy.

Various datasets and architectures are used to address gait recognition challenges.

Limitations remain in feature extraction and classification rates.

Abstract

In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits, and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.