Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition
This work addresses gait recognition for biometric identification, offering an incremental improvement by enhancing motion context modeling and noise correction.
The paper tackled gait recognition by proposing bilateral motion-oriented features and multi-scale temporal representations to better capture inter-frame walking patterns, achieving outstanding identification performance on CASIA-B and OU-MVLP datasets.
The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences. The relations between frames, however, have not received adequate attention in comparison to the intra-frame features. In this paper, motivated by optical flow, the bilateral motion-oriented features are proposed, which can allow the classic convolutional structure to have the capability to directly portray gait movement patterns at the feature level. Based on such features, we develop a set of multi-scale temporal representations that force the motion context to be richly described at various levels of temporal resolution. Furthermore, a correction block is devised to eliminate the segmentation noise of silhouettes for getting more precise gait information. Subsequently, the temporal feature set and the spatial features are combined to comprehensively characterize gait processes. Extensive experiments are conducted on CASIA-B and OU-MVLP datasets, and the results achieve an outstanding identification performance, which has demonstrated the effectiveness of the proposed approach.