  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.
