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Multimodal Gait Recognition Under Missing Modalities

Abstract : 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.
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https://hal-polytechnique.archives-ouvertes.fr/hal-03353572
Contributor : Vicky Kalogeiton Connect in order to contact the contributor
Submitted on : Friday, September 24, 2021 - 2:58:19 PM
Last modification on : Saturday, September 25, 2021 - 3:30:58 AM

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Ruben Delgado-Escano, Francisco Castro, Nicolas Guil, Vicky Kalogeiton, Manuel Marin-Jimenez. Multimodal Gait Recognition Under Missing Modalities. 2021 IEEE International Conference on Image Processing (ICIP), Sep 2021, Anchorage, Alaska (virtual), United States. ⟨10.1109/ICIP42928.2021.9506162⟩. ⟨hal-03353572⟩

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