A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges

TL;DR

This comprehensive survey reviews recent deep gait recognition methods, datasets, and challenges, highlighting advances, security concerns, and future directions in the field of biometric identification from gait patterns.

Contribution

The paper introduces a novel taxonomy for deep gait recognition based on deep representation learning and network architectures, providing a structured overview of recent advances.

Findings

Deep learning has significantly improved gait recognition accuracy.

In-the-wild gait recognition remains challenging due to environmental variability.

Security and privacy concerns are increasingly important in gait biometrics.

Abstract

Gait recognition aims to identify a person at a distance, serving as a promising solution for long-distance and less-cooperation pedestrian recognition. Recently, significant advancements in gait recognition have achieved inspiring success in many challenging scenarios by utilizing deep learning techniques. Against the backdrop that deep gait recognition has achieved almost perfect performance in laboratory datasets, much recent research has introduced new challenges for gait recognition, including robust deep representation modeling, in-the-wild gait recognition, and even recognition from new visual sensors such as infrared and depth cameras. Meanwhile, the increasing performance of gait recognition might also reveal concerns about biometrics security and privacy prevention for society. We provide a comprehensive survey on recent literature using deep learning and a discussion on the privacy and security of gait biometrics. This survey reviews the existing deep gait recognition methods through a novel view based on our proposed taxonomy. The proposed taxonomy differs from the conventional taxonomy of categorizing available gait recognition methods into the model- or appearance-based methods, while our taxonomic hierarchy considers deep gait recognition from two perspectives: deep representation learning and deep network architectures, illustrating the current approaches from both micro and macro levels. We also include up-to-date reviews of datasets and performance evaluations on diverse scenarios. Finally, we introduce privacy and security concerns on gait biometrics and discuss outstanding challenges and potential directions for future research.