  As a unique biometric feature that can be recognized at a distance, gait has
broad applications in crime prevention, forensic identification and social
security. To portray a gait, existing gait recognition methods utilize either a
gait template, where temporal information is hard to preserve, or a gait
sequence, which must keep unnecessary sequential constraints and thus loses the
flexibility of gait recognition. In this paper we present a novel perspective,
where a gait is regarded as a set consisting of independent frames. We propose
a new network named GaitSet to learn identity information from the set. Based
on the set perspective, our method is immune to permutation of frames, and can
naturally integrate frames from different videos which have been filmed under
different scenarios, such as diverse viewing angles, different clothes/carrying
conditions. Experiments show that under normal walking conditions, our
single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B
gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results
represent new state-of-the-art recognition accuracy. On various complex
scenarios, our model exhibits a significant level of robustness. It achieves
accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing
walking conditions, respectively. These outperform the existing best methods by
a large margin. The method presented can also achieve a satisfactory accuracy
with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with
only 7 frames. The source code has been released at
https://github.com/AbnerHqC/GaitSet.
