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Multi-Modal Human Authentication Using Silhouettes, Gait and RGB

2022-10-08 15:17:32
Yuxiang Guo, Cheng Peng, Chun Pong Lau, Rama Chellappa
arXiv_CV
       
Abstract

Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their advantages and drawbacks. In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis. The GaitPattern contributes to robust identification performance over a large range of viewing angles. Extensive experimental results on the CASIA-B dataset demonstrate that the proposed method outperforms state-of-the-art recognition systems. We also provide experimental results using the newly collected BRIAR dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2210.04050

PDF

https://arxiv.org/pdf/2210.04050.pdf

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