Face Restoration for Morphed Images Retouching

Creating a high-quality morphed image is a laborious and time-intensive endeavor due to the necessity of manual post-processing to eliminate typical artifacts produced by landmark-based morphing techniques. At the same time, morphed images of superior quality, without noticeable visual artifacts like ghosts, noise, or blurring, present heightened success probabilities to deceive human evaluators and commercial face verification systems. Therefore, in this paper, we investigate the use of Face Restoration to automatically retouch morphed images. Specifically, we investigate the efficacy of CodeFormer in removing artifacts and preserving the identity of the contributing subjects. An effective retouching method would allow the generation of large datasets containing high-quality retouched morphs, even starting from existing data, that are crucial for developing and evaluating the robustness of Morphing Attack Detection (MAD) algorithms.