# Face Image Quality Assessment: A Literature Survey

arXiv: https://arxiv.org/abs/2009.01103 · HTML: https://mlsanity.com/paper/2009.01103/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 30 of 80)

- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance](https://mlsanity.com/paper/2103.05977/) (2021)
- [Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs](https://mlsanity.com/paper/2302.02865/) (2023)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem](https://mlsanity.com/paper/2206.01881/) (2022)
- [Diffusion Model for Data-Driven Black-Box Optimization](https://mlsanity.com/paper/2403.13219/) (2024)
- [DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2305.05768/) (2023)
- [Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model](https://mlsanity.com/paper/2207.04904/) (2022)
- [FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration](https://mlsanity.com/paper/2212.02127/) (2022)
- [DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer](https://mlsanity.com/paper/2406.09622/) (2024)
- [Considerations on the Evaluation of Biometric Quality Assessment Algorithms](https://mlsanity.com/paper/2303.13294/) (2023)
- [QAGait: Revisit Gait Recognition from a Quality Perspective](https://mlsanity.com/paper/2401.13531/) (2024)
- [Pixel-Level Face Image Quality Assessment for Explainable Face Recognition](https://mlsanity.com/paper/2110.11001/) (2021)
- [An Image Quality Assessment Dataset for Portraits](https://mlsanity.com/paper/2304.05772/) (2023)
- [Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method](https://mlsanity.com/paper/2304.07056/) (2023)
- [CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model](https://mlsanity.com/paper/2403.05124/) (2024)
- [GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes](https://mlsanity.com/paper/2404.12203/) (2024)
- [FVQ: A Large-Scale Dataset and an LMM-based Method for Face Video Quality Assessment](https://mlsanity.com/paper/2504.09255/) (2025)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma](https://mlsanity.com/paper/2304.13680/) (2023)
- [ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset](https://mlsanity.com/paper/2404.11236/) (2024)
- [Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition](https://mlsanity.com/paper/2210.10354/) (2022)
- [FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance](https://mlsanity.com/paper/2111.02078/) (2021)
- [Toward Face Biometric De-identification using Adversarial Examples](https://mlsanity.com/paper/2302.03657/) (2023)
- [ViT-FIQA: Assessing Face Image Quality using Vision Transformers](https://mlsanity.com/paper/2508.13957/) (2025)
- [The Effect of Wearing a Face Mask on Face Image Quality](https://mlsanity.com/paper/2110.11283/) (2021)
- [3D Face Reconstruction: the Road to Forensics](https://mlsanity.com/paper/2309.11357/) (2023)
- [Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey](https://mlsanity.com/paper/2404.11159/) (2024)
- [Fun Selfie Filters in Face Recognition: Impact Assessment and Removal](https://mlsanity.com/paper/2202.06022/) (2022)
- [Assessing Bias in Face Image Quality Assessment](https://mlsanity.com/paper/2211.15265/) (2022)

## Abstract (arXiv)

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.