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Whole-body-based human authentication is a promising approach for remotebiometrics scenarios. Current literature focuses on either body recognitionbased on RGB images or gait recognition based on body shapes and walkingpatterns; both have their advantages and drawbacks. In this work, we proposeDual-Modal Ensemble (DME), which combines both RGB and silhouette data toachieve more robust performances for indoor and outdoor whole-body basedrecognition. Within DME, we propose GaitPattern, which is inspired by thedouble helical gait pattern used in traditional gait analysis. The GaitPatterncontributes to robust identification performance over a large range of viewingangles. Extensive experimental results on the CASIA-B dataset demonstrate thatthe proposed method outperforms state-of-the-art recognition systems. We alsoprovide experimental results using the newly collected BRIAR dataset.

Multi-Modal Human Authentication Using Silhouettes, Gait and RGB