Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the generative adversarial network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model.

翻译：COVID-19疫情蔓延所引发的全球大流行，给面部识别领域带来了全新维度的挑战——人们开始佩戴口罩。在此背景下，作者考虑利用机器学习中的图像修复技术来解决该问题，通过补全原本被口罩遮挡的面部轮廓，实现可能的完整人脸重建。具体而言，自编码器在保留图像重要全局特征方面具有巨大潜力，而生成对抗网络则展现出强大的生成能力。作者实现了两者的结合模型——上下文编码器，阐释了该模型如何融合两种算法的优势，并使用5万张网红人脸图像进行训练。实验取得了具有改进空间的坚实成果。此外，作者讨论了该模型存在的若干不足、可能的改进方案，以及面向应用前景的未来研究方向，同时提出了进一步优化与精炼模型的具体路径。