Rank-Based No-Reference Quality Assessment for Face Swapping

Face swapping has emerged as a significant research area in generative models due to rapid technological advancements. Current image quality assessment metrics for face swapping images depend on comparing facial attributes between fake and real images (e.g., target faces and source faces). However, obtaining such corresponding real images is often impractical. To address this challenge, we propose a no-reference image quality assessment method for face swapping. We observed that consistency among errors in various facial attributes can indicate overall image quality. Based on this insight, we created a large-scale face swapping dataset with multi-attribute ranking labels to produce quality ranking labels for objective attribute errors. We then trained a Siamese network to learn consistency patterns among these errors, demonstrating through experiments that these patterns align with human subjective judgments. Our method achieved state-of-the-art results in both coarse- and fine-grained tests. Finally, we applied this as a loss function to enhance existing face swapping models, leading to improvements in expression and pose attributes. Extensive experiments confirm the superiority of our method over existing general no-reference image quality assessment metrics and the latest metric for facial image quality assessment, making it well-suited for evaluating face swapping images in real-world scenarios.