Abstract
We propose a novel approach for individual recognition that uses the motion traits from face outline-anonymised videos as the identity signatures; we call this type of signature a Temporal-iD. To extract a robust Temporal-iD, a highly lightweight transformer-based model, namely the Temporal-iD-ViT (TiDViT), is devised to capture and aggregate Temporal-iD features from videos. The TiDViT is equipped with a custom-designed multi-head temporal–spatial joint attention module that establishes interaction between the current frame input and the previous hidden state, thereby temporally aggregating the temporal features. The TiDViT processes the face video frame by frame instead of in a fixed batch-wise manner, which requires less computational memory. Moreover, the TiDViT can extract the temporal features of unconstrained face videos and considers ethical and privacy concerns. Extensive experimental results show that the proposed TiDViT model achieves a decent performance on this highly challenging task.
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