GAN-based Algorithm for Efficient Image Inpainting
This addresses a domain-specific challenge in facial recognition for masked faces, but it is incremental as it combines existing methods without major innovation.
The authors tackled the problem of facial recognition with masks during the COVID-19 pandemic by using image inpainting to complete faces covered by masks, implementing a combination of autoencoder and GAN models trained on 50,000 influencer face images and yielding solid results with room for improvement.
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.