论文标题
使用轮廓，步态和RGB的多模式人类身份验证
Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
论文作者
论文摘要
基于全体的人类身份验证是远程生物识别方案的一种有前途的方法。当前的文献侧重于基于RGB图像或基于身体形状和步行模式的步态识别的身体识别；两者都有其优势和缺点。在这项工作中，我们提出了双模式集合（DME），它结合了RGB和Silhouette数据，以实现室内和室外全身识别的更强大的性能。在DME中，我们提出了gaitpattern，它的灵感来自传统步态分析中使用的双螺旋步态模式。 gaitpatern在各种视角上有助于稳健的识别性能。 CASIA-B数据集的广泛实验结果表明，所提出的方法的表现优于最先进的识别系统。我们还使用新收集的Briar数据集提供实验结果。
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.
