Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
This work addresses remote biometric authentication for security applications, presenting an incremental improvement over existing methods.
The paper tackles whole-body human authentication by combining RGB and silhouette data with a novel gait pattern method, achieving state-of-the-art performance on the CASIA-B dataset and testing on the BRIAR dataset.
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.