Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
This work addresses gait recognition for person authentication and security, but it is incremental as it builds on existing backbones like GaitGL.
The paper tackles the problem of gait recognition under viewpoint and clothing changes by proposing a novel motion modeling method that extracts and enhances motion features from gait sequences, achieving superior performance on cross-view datasets.
As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. This paper proposes a novel motion modeling method to extract the discriminative and robust representation. Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer. Then we continuously enhance the motion feature in deep layers. This motion modeling approach is independent of mainstream work in building network architectures. As a result, one can apply this motion modeling method to any backbone to improve gait recognition performance. In this paper, we combine motion modeling with one commonly used backbone~(GaitGL) as GaitGL-M to illustrate motion modeling. Extensive experimental results on two commonly-used cross-view gait datasets demonstrate the superior performance of GaitGL-M over existing state-of-the-art methods.