GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set
This work provides a more robust and flexible gait recognition method for security and identification applications, particularly in challenging real-world conditions with varying views, clothing, and carried items.
This paper addresses cross-view gait recognition by treating gait as a deep set of frames, integrating them with a global-local fused deep network. The method achieves a rank-1 accuracy of 96.1% on CASIA-B and 87.9% on OU-MVLP under normal walking conditions, and significantly outperforms existing methods in complex scenarios like bag-carrying (90.8%) and coat-wearing (70.3%) on CASIA-B.
Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad applications in crime prevention, forensic identification, and social security. To portray a gait, existing gait recognition methods utilize either a gait template which makes it difficult to preserve temporal information, or a gait sequence that maintains unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper, we present a novel perspective that utilizes gait as a deep set, which means that a set of gait frames are integrated by a global-local fused deep network inspired by the way our left- and right-hemisphere processes information to learn information that can be used in identification. Based on this deep set perspective, our method is immune to frame permutations, and can naturally integrate frames from different videos that have been acquired under different scenarios, such as diverse viewing angles, different clothes, or different item-carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset. Under various complex scenarios, our model also exhibits a high level of robustness. It achieves accuracies of 90.8% and 70.3% on CASIA-B under bag-carrying and coat-wearing walking conditions respectively, significantly outperforming the best existing methods. Moreover, the proposed method maintains a satisfactory accuracy even when only small numbers of frames are available in the test samples; for example, it achieves 85.0% on CASIA-B even when using only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.