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

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