The pandemic of these very recent years has led to a dramatic increase in people wearing protective masks in public venues. This poses obvious challenges to the pervasive use of face recognition technology that now is suffering a decline in performance. One way to address the problem is to revert to face recovery methods as a preprocessing step. Current approaches to face reconstruction and manipulation leverage the ability to model the face manifold, but tend to be generic. We introduce a method that is specific for the recovery of the face image from an image of the same individual wearing a mask. We do so by designing a specialized GAN inversion method, based on an appropriate set of losses for learning an unmasking encoder. With extensive experiments, we show that the approach is effective at unmasking face images. In addition, we also show that the identity information is preserved sufficiently well to improve face verification performance based on several face recognition benchmark datasets.
翻译:近年来的流行病导致在公共场所佩戴防护面具的人数急剧增加,这对普遍使用面部识别技术提出了明显挑战,目前这种技术的性能正在下降。解决问题的一个办法是将恢复方法作为预处理步骤,重新面对回收方法。目前面对重建和操纵的方法利用了模拟面部的多重能力,但往往比较一般。我们采用了一种具体的方法,从戴面具的同一个人的图像中恢复面部图像。我们这样做的方法是设计一种专门的GAN反向法,其依据是学习一个不显眼的编码器所需的一套适当的损失。我们通过广泛的实验表明,这种方法在解脸图像方面是有效的。此外,我们还表明,身份信息保留得足够好,以便根据几个面部识别基准数据集改进面部核查工作。