  Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual
quality of facial images, which is crucial in improving image restoration
algorithms and selecting high-quality face images for downstream tasks. We
present a novel transformer-based method for GFIQA, which is aided by two
unique mechanisms. First, a Dual-Set Degradation Representation Learning (DSL)
mechanism uses facial images with both synthetic and real degradations to
decouple degradation from content, ensuring generalizability to real-world
scenarios. This self-supervised method learns degradation features on a global
scale, providing a robust alternative to conventional methods that use local
patch information in degradation learning. Second, our transformer leverages
facial landmarks to emphasize visually salient parts of a face image in
evaluating its perceptual quality. We also introduce a balanced and diverse
Comprehensive Generic Face IQA (CGFIQA-40k) dataset of 40K images carefully
designed to overcome the biases, in particular the imbalances in skin tone and
gender representation, in existing datasets. Extensive analysis and evaluation
demonstrate the robustness of our method, marking a significant improvement
over prior methods.
