MULTIMODAL GAIT RECOGNITION UNDER MISSING MODALITIES

Multimodal systems for gait recognition have gained a lot of attention. However, there is a clear gap in the study of missing modalities, which represents real-life scenarios where sensors fail or data get corrupted. Here, we investigate how to handle missing modalities for gait recognition. We propose a single and flexible framework that uses a variable number of input modalities. For each modality, it consists of a branch and a binary unit indicating whether the modality is available; these are gated and merged together. Finally, it generates a single and compact `multimodal' gait signature that encodes biometric information of the input. Our framework outperforms the state of the art on TUM-GAID and extensive experiments reveal its effectiveness for handling missing modalities even in the multiview setup of CASIA-B. The code is available online: https://github.com/avagait/gaitmiss.

Automated summary

This study focuses on the issue of missing modalities in multimodal gait recognition systems, proposing a framework that uses a variable number of input modalities and handles missing modalities through branches and binary units. Experimental results confirm the effectiveness of the framework in dealing with missing modalities.