# Considerations on the Evaluation of Biometric Quality Assessment Algorithms arXiv: https://arxiv.org/abs/2303.13294 ยท HTML: https://mlsanity.com/paper/2303.13294/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 10 of its 31 references that are in the map. - First posted on arXiv 23 Mar 2023. ## Builds on (most cited first, 10 of 10) - [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017) - [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014) - [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (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) - [Low-Resolution Face Recognition](https://mlsanity.com/paper/1811.08965/) (2018) - [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.07698/) (2018) - [Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge](https://mlsanity.com/paper/1712.09401/) (2017) - [MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence](https://mlsanity.com/paper/2106.05601/) (2021) ## Built on by (most cited first, 16 of 16) - [DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2305.05768/) (2023) - [ViT-FIQA: Assessing Face Image Quality using Vision Transformers](https://mlsanity.com/paper/2508.13957/) (2025) - [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) - [Double Trouble? Impact and Detection of Duplicates in Face Image Datasets](https://mlsanity.com/paper/2401.14088/) (2024) - [Radial Distortion in Face Images: Detection and Impact](https://mlsanity.com/paper/2501.07179/) (2025) - [Predictive Quality Assessment for Mobile Secure Graphics](https://mlsanity.com/paper/2509.20028/) (2025) - [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) - [Image Quality Assessment of Identity Cards Using Measures from Open Face Image Quality](https://mlsanity.com/paper/2606.11884/) (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) - [Infant Fingerprints: Quality Assessment & Recognition](https://mlsanity.com/paper/2610.20432/) (2026) ## Abstract (arXiv) Quality assessment algorithms can be used to estimate the utility of a biometric sample for the purpose of biometric recognition. "Error versus Discard Characteristic" (EDC) plots, and "partial Area Under Curve" (pAUC) values of curves therein, are generally used by researchers to evaluate the predictive performance of such quality assessment algorithms. An EDC curve depends on an error type such as the "False Non Match Rate" (FNMR), a quality assessment algorithm, a biometric recognition system, a set of comparisons each corresponding to a biometric sample pair, and a comparison score threshold corresponding to a starting error. To compute an EDC curve, comparisons are progressively discarded based on the associated samples' lowest quality scores, and the error is computed for the remaining comparisons. Additionally, a discard fraction limit or range must be selected to compute pAUC values, which can then be used to quantitatively rank quality assessment algorithms. This paper discusses and analyses various details for this kind of quality assessment algorithm evaluation, including general EDC properties, interpretability improvements for pAUC values based on a hard lower error limit and a soft upper error limit, the use of relative instead of discrete rankings, stepwise vs. linear curve interpolation, and normalisation of quality scores to a [0, 100] integer range. We also analyse the stability of quantitative quality assessment algorithm rankings based on pAUC values across varying pAUC discard fraction limits and starting errors, concluding that higher pAUC discard fraction limits should be preferred. The analyses are conducted both with synthetic data and with real face image and fingerprint data, with a focus on general modality-independent conclusions for EDC evaluations. Various EDC alternatives are discussed as well.