FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration

TL;DR

FaceQAN introduces a novel face image quality assessment method leveraging adversarial noise analysis, linking image quality to adversarial attacks, and demonstrating competitive performance across multiple datasets and models.

Contribution

This paper presents the first approach to link face image quality assessment with adversarial noise analysis, enhancing reliability and robustness in face recognition systems.

Findings

FaceQAN achieves competitive results on benchmark datasets.

The method effectively links image quality to adversarial attack characteristics.

FaceQAN demonstrates robustness across multiple face recognition models.

Abstract

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.