论文标题
通过功能纠正的统一框架，用于掩盖和无掩模的面部识别
A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification
论文作者
论文摘要
现在，在理想条件下的面部识别被认为是解决深度学习进展的问题。然而，认识到面孔仍然是一个挑战。现有的技术通常无法识别面罩覆盖的口腔和鼻子的面孔，现在在19009年大流行中非常普遍。解决此问题的常见方法包括1）在识别过程中丢弃蒙面区域的信息，以及2）在识别之前恢复蒙面区域。很少有作品认为从掩盖面部提取的特征与从其无面罩的配料中提取的特征之间的一致性。这导致了经过训练的模型，以识别蒙面面孔经常在无面孔上表现出降解的性能。在本文中，我们提出了一个统一的框架，称为“面部特征整流网络”（FFR-net），用于识别蒙面和无面膜的面孔。我们介绍了纠正块，以纠正由空间和通道尺寸的最先进的识别模型提取的特征，以最大程度地减少蒙面的面部及其无面膜的距离之间的距离。实验表明，我们的统一框架可以学习一个纠正的特征空间，以有效地识别掩盖面孔和无面膜，从而实现最新的结果。项目代码：https：//github.com/haoosz/ffr-net
Face recognition under ideal conditions is now considered a well-solved problem with advances in deep learning. Recognizing faces under occlusion, however, still remains a challenge. Existing techniques often fail to recognize faces with both the mouth and nose covered by a mask, which is now very common under the COVID-19 pandemic. Common approaches to tackle this problem include 1) discarding information from the masked regions during recognition and 2) restoring the masked regions before recognition. Very few works considered the consistency between features extracted from masked faces and from their mask-free counterparts. This resulted in models trained for recognizing masked faces often showing degraded performance on mask-free faces. In this paper, we propose a unified framework, named Face Feature Rectification Network (FFR-Net), for recognizing both masked and mask-free faces alike. We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions, to minimize the distance between a masked face and its mask-free counterpart in the rectified feature space. Experiments show that our unified framework can learn a rectified feature space for recognizing both masked and mask-free faces effectively, achieving state-of-the-art results. Project code: https://github.com/haoosz/FFR-Net
