Deep Gait Recognition: A Survey

TL;DR

This survey reviews recent advances in deep learning-based gait recognition, highlighting datasets, methods, challenges, and future directions, emphasizing its growing importance in biometric identification.

Contribution

It introduces a novel taxonomy for gait recognition research and provides a comprehensive overview of deep learning methods, datasets, and challenges in the field.

Findings

Deep learning dominates gait recognition state-of-the-art.

The proposed taxonomy organizes research into four key dimensions.

Identified challenges and promising future research directions.

Abstract

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.