  Face swapping has become a prominent research area in computer vision and
image processing due to rapid technological advancements. The metric of
measuring the quality in most face swapping methods relies on several distances
between the manipulated images and the source image, or the target image, i.e.,
there are suitable known reference face images. Therefore, there is still a gap
in accurately assessing the quality of face interchange in reference-free
scenarios. In this study, we present a novel no-reference image quality
assessment (NR-IQA) method specifically designed for face swapping, addressing
this issue by constructing a comprehensive large-scale dataset, implementing a
method for ranking image quality based on multiple facial attributes, and
incorporating a Siamese network based on interpretable qualitative comparisons.
Our model demonstrates the state-of-the-art performance in the quality
assessment of swapped faces, providing coarse- and fine-grained. Enhanced by
this metric, an improved face-swapping model achieved a more advanced level
with respect to expressions and poses. Extensive experiments confirm the
superiority of our method over existing general no-reference image quality
assessment metrics and the latest metric of facial image quality assessment,
making it well suited for evaluating face swapping images in real-world
scenarios.
