  Gait recognition is a biometric technology that has received extensive
attention. Most existing gait recognition algorithms are unimodal, and a few
multimodal gait recognition algorithms perform multimodal fusion only once.
None of these algorithms may fully exploit the complementary advantages of the
multiple modalities. In this paper, by considering the temporal and spatial
characteristics of gait data, we propose a multi-stage feature fusion strategy
(MSFFS), which performs multimodal fusions at different stages in the feature
extraction process. Also, we propose an adaptive feature fusion module (AFFM)
that considers the semantic association between silhouettes and skeletons. The
fusion process fuses different silhouette areas with their more related
skeleton joints. Since visual appearance changes and time passage co-occur in a
gait 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 linkages
information at different spatial scales. Combining the strategy and modules
mentioned above, we propose a multi-stage adaptive feature fusion (MSAFF)
neural network, which shows state-of-the-art performance in many experiments on
three datasets. Besides, MSAFF is equipped with feature dimensional pooling (FD
Pooling), which can significantly reduce the dimension of the gait
representations without hindering the accuracy.
https://github.com/ShinanZou/MSAFF
