  Modern face recognition (FR) models excel in constrained scenarios, but often
suffer from decreased performance when deployed in unconstrained (real-world)
environments due to uncertainties surrounding the quality of the captured
facial data. Face image quality assessment (FIQA) techniques aim to mitigate
these performance degradations by providing FR models with sample-quality
predictions that can be used to reject low-quality samples and reduce false
match errors. However, despite steady improvements, ensuring reliable quality
estimates across facial images with diverse characteristics remains
challenging. In this paper, we present a powerful new FIQA approach, named
DifFIQA, which relies on denoising diffusion probabilistic models (DDPM) and
ensures highly competitive results. The main idea behind the approach is to
utilize the forward and backward processes of DDPMs to perturb facial images
and quantify the impact of these perturbations on the corresponding image
embeddings for quality prediction. Because the diffusion-based perturbations
are computationally expensive, we also distill the knowledge encoded in DifFIQA
into a regression-based quality predictor, called DifFIQA(R), that balances
performance and execution time. We evaluate both models in comprehensive
experiments on 7 datasets, with 4 target FR models and against 10
state-of-the-art FIQA techniques with highly encouraging results. The source
code will be made publicly available.
