  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.
