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arXiv · 2310.15946

ShARc: Shape and Appearance Recognition for Person Identification In-the-wild

Haidong Zhu·Wanrong Zheng·Zhaoheng Zheng·Ram Nevatia
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Abstract

Identifying individuals in unconstrained video settings is a valuable yet challenging task in biometric analysis due to variations in appearances, environments, degradations, and occlusions. In this paper, we present ShARc, a multimodal approach for video-based person identification in uncontrolled environments that emphasizes 3-D body shape, pose, and appearance. We introduce two encoders: a Pose and Shape Encoder (PSE) and an Aggregated Appearance Encoder (AAE). PSE encodes the body shape via binarized silhouettes, skeleton motions, and 3-D body shape, while AAE provides two levels of temporal appearance feature aggregation: attention-based feature aggregation and averaging aggregation. For attention-based feature aggregation, we employ spatial and temporal attention to focus on key areas for person distinction. For averaging aggregation, we introduce a novel flattening layer after averaging to extract more distinguishable information and reduce overfitting of attention. We utilize centroid feature averaging for gallery registration. We demonstrate significant improvements over existing state-of-the-art methods on public datasets, including CCVID, MEVID, and BRIAR.

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Haidong Zhu, Wanrong Zheng, Zhaoheng Zheng, Ram Nevatia. 2023-10-24. ShARc: Shape and Appearance Recognition for Person Identification In-the-wild. https://arxiv.org/abs/2310.15946

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