# FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration

arXiv: https://arxiv.org/abs/2212.02127 · HTML: https://mlsanity.com/paper/2212.02127/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 24 of 24)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://mlsanity.com/paper/2004.00288/) (2020)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness](https://mlsanity.com/paper/2003.09373/) (2020)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability](https://mlsanity.com/paper/2112.06592/) (2021)
- [SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance](https://mlsanity.com/paper/2103.05977/) (2021)
- [FaceQnet: Quality Assessment for Face Recognition based on Deep Learning](https://mlsanity.com/paper/1904.01740/) (2019)
- [Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/2108.10290/) (2021)
- [MFR 2021: Masked Face Recognition Competition](https://mlsanity.com/paper/2106.15288/) (2021)
- [Face hallucination using cascaded super-resolution and identity priors](https://mlsanity.com/paper/1805.10938/) (2018)
- [Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations](https://mlsanity.com/paper/1710.01494/) (2017)
- [Inducing Predictive Uncertainty Estimation for Face Recognition](https://mlsanity.com/paper/2009.00603/) (2020)
- [Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition](https://mlsanity.com/paper/2201.04011/) (2022)

## Built on by (most cited first, 18 of 18)

- [DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2305.05768/) (2023)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [ViT-FIQA: Assessing Face Image Quality using Vision Transformers](https://mlsanity.com/paper/2508.13957/) (2025)
- [Optimization-Based Improvement of Face Image Quality Assessment Techniques](https://mlsanity.com/paper/2305.14856/) (2023)
- [FROQ: Observing Face Recognition Models for Efficient Quality Assessment](https://mlsanity.com/paper/2509.17689/) (2025)
- [ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers](https://mlsanity.com/paper/2601.05741/) (2026)
- [CreativeVR: Diffusion-Prior-Guided Approach for Structure and Motion Restoration in Generative and Real Videos](https://mlsanity.com/paper/2512.12060/) (2025)
- [AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation](https://mlsanity.com/paper/2404.09555/) (2024)
- [Impact of Face Alignment on Face Image Quality](https://mlsanity.com/paper/2412.11779/) (2024)
- [Quality-Aware Prototype Memory for Face Representation Learning](https://mlsanity.com/paper/2311.07734/) (2023)
- [ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers](https://mlsanity.com/paper/2604.22841/) (2026)
- [EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment](https://mlsanity.com/paper/2604.22842/) (2026)
- [FunFace: Feature Utility and Norm Estimation for Face Recognition](https://mlsanity.com/paper/2604.26598/) (2026)
- [PreFIQs: Face Image Quality Is What Survives Pruning](https://mlsanity.com/paper/2605.13396/) (2026)
- [Employing Vision-Language Models for Face Image Quality Assessment](https://mlsanity.com/paper/2605.17489/) (2026)
- [Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment](https://mlsanity.com/paper/2607.22752/) (2026)
- [Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)](https://mlsanity.com/paper/2609.02644/) (2026)
- [Learning Steadily: Accumulating Relative Point Margin Scores for Face Image Quality Assessment](https://mlsanity.com/paper/2609.31662/) (2026)

## Abstract (arXiv)

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.