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.
@article{arxiv.2210.04050,
title = {Multi-Modal Human Authentication Using Silhouettes, Gait and RGB},
author = {Yuxiang Guo and Cheng Peng and Chun Pong Lau and Rama Chellappa},
journal= {arXiv preprint arXiv:2210.04050},
year = {2022}
}