Research on Occluded Face Restoration Based on Denoising Diffusion Modeling

Although face recognition technology is developing rapidly, it still faces many challenges in complex environments. For example, masks obscure key features of the face, making it difficult for the traditional face recognition technology to accurately recognize individuals. Especially during the epidemic, wearing masks and other coverings has become the norm, and how to accurately recognize obscured face information has become an urgent problem. Therefore, it is important to investigate how to repair occluded face images to unoccluded real face images. This paper proposes an occluded face image restoration technique based on denoising diffusion model, which is improved by combining with REPAINT technique to explore the accuracy of the renoising method for face restoration. The final comparison is made based on the test results and FID scores, where the improved DDPM model can achieve a minimum FID score of 7.563, It has been proved to have better ability in repairing human faces. This research can effectively improve the applicability and accuracy of face recognition systems in complex environments, and promote the wide application of identification technology in the field of intelligent surveillance and financial payment.