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Gait recognition is a biometric technology that has received extensiveattention. Most existing gait recognition algorithms are unimodal, and a fewmultimodal gait recognition algorithms perform multimodal fusion only once.None of these algorithms may fully exploit the complementary advantages of themultiple modalities. In this paper, by considering the temporal and spatialcharacteristics of gait data, we propose a multi-stage feature fusion strategy(MSFFS), which performs multimodal fusions at different stages in the featureextraction process. Also, we propose an adaptive feature fusion module (AFFM)that considers the semantic association between silhouettes and skeletons. Thefusion process fuses different silhouette areas with their more relatedskeleton joints. Since visual appearance changes and time passage co-occur in agait period, we propose a multiscale spatial-temporal feature extractor(MSSTFE) to learn the spatial-temporal linkage features thoroughly.Specifically, MSSTFE extracts and aggregates spatial-temporal linkagesinformation at different spatial scales. Combining the strategy and modulesmentioned above, we propose a multi-stage adaptive feature fusion (MSAFF)neural network, which shows state-of-the-art performance in many experiments onthree datasets. Besides, MSAFF is equipped with feature dimensional pooling (FDPooling), which can significantly reduce the dimension of the gaitrepresentations without hindering the accuracy.https://github.com/ShinanZou/MSAFF

A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition