  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.

Computer Vision and Pattern Recognition;Image and Video Processing