# Biometric Quality: Review and Application to Face Recognition with FaceQnet

arXiv: https://arxiv.org/abs/2006.03298 · HTML: https://mlsanity.com/paper/2006.03298/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 30 of 42)

- [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)
- [CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning](https://mlsanity.com/paper/2112.00686/) (2021)
- [IFQA: Interpretable Face Quality Assessment](https://mlsanity.com/paper/2211.07077/) (2022)
- [DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2305.05768/) (2023)
- [Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation](https://mlsanity.com/paper/2304.10066/) (2023)
- [On the Applicability of Synthetic Data for Face Recognition](https://mlsanity.com/paper/2104.02815/) (2021)
- [A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics](https://mlsanity.com/paper/2110.11111/) (2021)
- [What Should Be Balanced in a "Balanced" Face Recognition Dataset?](https://mlsanity.com/paper/2304.09818/) (2023)
- [GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes](https://mlsanity.com/paper/2404.12203/) (2024)
- [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)
- [Harnessing Unrecognizable Faces for Improving Face Recognition](https://mlsanity.com/paper/2106.04112/) (2021)
- [The Effect of Wearing a Face Mask on Face Image Quality](https://mlsanity.com/paper/2110.11283/) (2021)
- [Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models](https://mlsanity.com/paper/2112.04827/) (2021)
- [3D Face Reconstruction: the Road to Forensics](https://mlsanity.com/paper/2309.11357/) (2023)
- [Generation of Non-Deterministic Synthetic Face Datasets Guided by Identity Priors](https://mlsanity.com/paper/2112.03632/) (2021)
- [CAT: Controllable Attribute Translation for Fair Facial Attribute Classification](https://mlsanity.com/paper/2209.06850/) (2022)
- [Does lossy image compression affect racial bias within face recognition?](https://mlsanity.com/paper/2208.07613/) (2022)
- [FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment](https://mlsanity.com/paper/2201.00770/) (2022)
- [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)
- [Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data](https://mlsanity.com/paper/2208.08207/) (2022)
- [Impact of Facial Tattoos and Paintings on Face Recognition Systems](https://mlsanity.com/paper/2103.09939/) (2021)
- [Effect of Lossy Compression Algorithms on Face Image Quality and Recognition](https://mlsanity.com/paper/2302.12593/) (2023)
- [Pose Impact Estimation on Face Recognition using 3D-Aware Synthetic Data with Application to Quality Assessment](https://mlsanity.com/paper/2303.00491/) (2023)
- [AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation](https://mlsanity.com/paper/2404.09555/) (2024)
- [FaceOracle: Chat with a Face Image Oracle](https://mlsanity.com/paper/2501.07202/) (2025)
- [MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training](https://mlsanity.com/paper/2508.07590/) (2025)

## Abstract (arXiv)

"The output of a computerised system can only be as accurate as the information entered into it." This rather trivial statement is the basis behind one of the driving concepts in biometric recognition: biometric quality. Quality is nowadays widely regarded as the number one factor responsible for the good or bad performance of automated biometric systems. It refers to the ability of a biometric sample to be used for recognition purposes and produce consistent, accurate, and reliable results. Such a subjective term is objectively estimated by the so-called biometric quality metrics. These algorithms play nowadays a pivotal role in the correct functioning of systems, providing feedback to the users and working as invaluable audit tools. In spite of their unanimously accepted relevance, some of the most used and deployed biometric characteristics are lacking behind in the development of these methods. This is the case of face recognition. After a gentle introduction to the general topic of biometric quality and a review of past efforts in face quality metrics, in the present work, we address the need for better face quality metrics by developing FaceQnet. FaceQnet is a novel open-source face quality assessment tool, inspired and powered by deep learning technology, which assigns a scalar quality measure to facial images, as prediction of their recognition accuracy. Two versions of FaceQnet have been thoroughly evaluated both in this work and also independently by NIST, showing the soundness of the approach and its competitiveness with respect to current state-of-the-art metrics. Even though our work is presented here particularly in the framework of face biometrics, the proposed methodology for building a fully automated quality metric can be very useful and easily adapted to other artificial intelligence tasks.