Domain adaptation for inpainting-based face recognition studies

Recent inpainting methods have demonstrated im-pressive outcomes in filling missing parts of images, especially for reconstructing facial areas obscured by occlusions. However, studies show that these models are not adequately effective in real-world applications, primarily due to data bias and the distribution of faces in images. This research focuses on domain adaptation of the commonly used Labeled Faces in the Wild (LFW) dataset, employing the Mask-Aware Transformer (MAT) inpainting method for reconstructing occluded facial regions and examining its impact on facial recognition accuracy. Three types of generated masks were applied to specific facial areas, covering key points on the face, using three datasets: CelebA-HQ, LFW, and a specially adapted LFW. The analysis employed various metrics to assess the quality of the reconstruction. The results indicate that applying a simple adaptation method to the LFW dataset significantly boosts facial recognition capabilities, with improvements reaching up to 16.43% compared to the original LFW. Subsequently, the experiments demonstrate that using inpainting methods enhances face recognition considerably when compared to images with applied masks without reconstruction. Notably, improvements in positively verified images were ob-served up to 89.30% for CelebA-HQ, 21.37% for LFW, and 29.69% for the adapted LFW.