  In recent years, Face Image Quality Assessment (FIQA) has become an
indispensable part of the face recognition system to guarantee the stability
and reliability of recognition performance in an unconstrained scenario. For
this purpose, the FIQA method should consider both the intrinsic property and
the recognizability of the face image. Most previous works aim to estimate the
sample-wise embedding uncertainty or pair-wise similarity as the quality score,
which only considers the information from partial intra-class. However, these
methods ignore the valuable information from the inter-class, which is for
estimating to the recognizability of face image. In this work, we argue that a
high-quality face image should be similar to its intra-class samples and
dissimilar to its inter-class samples. Thus, we propose a novel unsupervised
FIQA method that incorporates Similarity Distribution Distance for Face Image
Quality Assessment (SDD-FIQA). Our method generates quality pseudo-labels by
calculating the Wasserstein Distance (WD) between the intra-class similarity
distributions and inter-class similarity distributions. With these quality
pseudo-labels, we are capable of training a regression network for quality
prediction. Extensive experiments on benchmark datasets demonstrate that the
proposed SDD-FIQA surpasses the state-of-the-arts by an impressive margin.
Meanwhile, our method shows good generalization across different recognition
systems.
