论文标题
大流行时期的生物识别技术：40％的蒙版面部识别降解可以降低至2％
Biometrics in the Time of Pandemic: 40% Masked Face Recognition Degradation can be Reduced to 2%
论文作者
论文摘要
在这项对使用Flickr-Faces-HQ和SpeaphFaces数据集产生的掩盖面孔和透明面部面孔的面部识别的研究中，我们报告了在BONDEMICS时，特别是在边境检查点方案中，由戴面膜戴的面具引起的识别性能降低了36.78％。我们使用跨光谱域中的先进深度学习方法实现了更好的性能，并将降解降低到1.79％。
In this study of the face recognition on masked versus unmasked faces generated using Flickr-Faces-HQ and SpeakingFaces datasets, we report 36.78% degradation of recognition performance caused by the mask-wearing at the time of pandemics, in particular, in border checkpoint scenarios. We have achieved better performance and reduced the degradation to 1.79% using advanced deep learning approaches in the cross-spectral domain.
