# A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics arXiv: https://arxiv.org/abs/2110.11111 ยท HTML: https://mlsanity.com/paper/2110.11111/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 57 references that are in the map. - First posted on arXiv 21 Oct 2021. ## Builds on (most cited first, 23 of 23) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017) - [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016) - [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017) - [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016) - [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019) - [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) - [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019) - [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016) - [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019) - [Deep Pyramidal Residual Networks](https://mlsanity.com/paper/1610.02915/) (2016) - [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017) - [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021) - [Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild](https://mlsanity.com/paper/2005.13983/) (2020) - [SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness](https://mlsanity.com/paper/2003.09373/) (2020) - [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019) - [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) - [Biometric Quality: Review and Application to Face Recognition with FaceQnet](https://mlsanity.com/paper/2006.03298/) (2020) - [Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition](https://mlsanity.com/paper/2004.01019/) (2020) - [Predicting Face Recognition Performance Using Image Quality](https://mlsanity.com/paper/1510.07119/) (2015) ## Built on by (most cited first, 13 of 13) - [CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability](https://mlsanity.com/paper/2112.06592/) (2021) - [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022) - [GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes](https://mlsanity.com/paper/2404.12203/) (2024) - [Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality](https://mlsanity.com/paper/2208.05864/) (2022) - [ViT-FIQA: Assessing Face Image Quality using Vision Transformers](https://mlsanity.com/paper/2508.13957/) (2025) - [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) - [ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers](https://mlsanity.com/paper/2601.05741/) (2026) - [ScaleFace: Uncertainty-aware Deep Metric Learning](https://mlsanity.com/paper/2209.01880/) (2022) - [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022) - [MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training](https://mlsanity.com/paper/2508.07590/) (2025) - [A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss](https://mlsanity.com/paper/2509.10114/) (2025) - [PreFIQs: Face Image Quality Is What Survives Pruning](https://mlsanity.com/paper/2605.13396/) (2026) - [Learning Steadily: Accumulating Relative Point Margin Scores for Face Image Quality Assessment](https://mlsanity.com/paper/2609.31662/) (2026) ## Abstract (arXiv) Quality scores provide a measure to evaluate the utility of biometric samples for biometric recognition. Biometric recognition systems require high-quality samples to achieve optimal performance. This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics. While face-specific metrics rely on features of aligned face images, general image quality metrics can be used on the global image and relate to human perceptions. In this paper, we analyze the gap between the general image quality metrics and the face image quality metrics. Our contribution lies in a thorough examination of how different the image quality assessment algorithms relate to the utility for the face recognition task. The results of image quality assessment algorithms are further compared with those of dedicated face image quality assessment algorithms. In total, 25 different quality metrics are evaluated on three face image databases, BioSecure, LFW, and VGGFace2 using three open-source face recognition solutions, SphereFace, ArcFace, and FaceNet. Our results reveal a clear correlation between learned image metrics to face image utility even without being specifically trained as a face utility measure. Individual handcrafted features lack general stability and perform significantly worse than general face-specific quality metrics. We additionally provide a visual insight into the image areas contributing to the quality score of a selected set of quality assessment methods.