Kavli Affiliate: Cheng Peng
| First 5 Authors: Yuxiang Guo, Cheng Peng, Chun Pong Lau, Rama Chellappa,
| Summary:
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.
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