  Recent state-of-the-art face recognition (FR) approaches have achieved
impressive performance, yet unconstrained face recognition still represents an
open problem. Face image quality assessment (FIQA) approaches aim to estimate
the quality of the input samples that can help provide information on the
confidence of the recognition decision and eventually lead to improved results
in challenging scenarios. While much progress has been made in face image
quality assessment in recent years, computing reliable quality scores for
diverse facial images and FR models remains challenging. In this paper, we
propose a novel approach to face image quality assessment, called FaceQAN, that
is based on adversarial examples and relies on the analysis of adversarial
noise which can be calculated with any FR model learned by using some form of
gradient descent. As such, the proposed approach is the first to link image
quality to adversarial attacks. Comprehensive (cross-model as well as
model-specific) experiments are conducted with four benchmark datasets, i.e.,
LFW, CFP-FP, XQLFW and IJB-C, four FR models, i.e., CosFace, ArcFace,
CurricularFace and ElasticFace, and in comparison to seven state-of-the-art
FIQA methods to demonstrate the performance of FaceQAN. Experimental results
show that FaceQAN achieves competitive results, while exhibiting several
desirable characteristics.
