# EQFace: A Simple Explicit Quality Network for Face Recognition

arXiv: https://arxiv.org/abs/2105.00634 · HTML: https://mlsanity.com/paper/2105.00634/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 16 of 16)

- [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)
- [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)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Towards Universal Representation Learning for Deep Face Recognition](https://mlsanity.com/paper/2002.11841/) (2020)
- [The Devil of Face Recognition is in the Noise](https://mlsanity.com/paper/1807.11649/) (2018)
- [FaceQnet: Quality Assessment for Face Recognition based on Deep Learning](https://mlsanity.com/paper/1904.01740/) (2019)
- [Learning with Bounded Instance- and Label-dependent Label Noise](https://mlsanity.com/paper/1709.03768/) (2017)
- [Neural Aggregation Network for Video Face Recognition](https://mlsanity.com/paper/1603.05474/) (2016)
- [Quality Aware Network for Set to Set Recognition](https://mlsanity.com/paper/1704.03373/) (2017)
- [Noise-Tolerant Paradigm for Training Face Recognition CNNs](https://mlsanity.com/paper/1903.10357/) (2019)
- [Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition](https://mlsanity.com/paper/1804.01159/) (2018)
- [Automatic Face Image Quality Prediction](https://mlsanity.com/paper/1706.09887/) (2017)

## Built on by (most cited first, 10 of 10)

- [QMagFace: Simple and Accurate Quality-Aware Face Recognition](https://mlsanity.com/paper/2111.13475/) (2021)
- [Adversarial Relighting Against Face Recognition](https://mlsanity.com/paper/2108.07920/) (2021)
- [Identity-Preserving Aging of Face Images via Latent Diffusion Models](https://mlsanity.com/paper/2307.08585/) (2023)
- [Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition](https://mlsanity.com/paper/2210.10354/) (2022)
- [Exploring Spatial-Temporal Features for Deepfake Detection and Localization](https://mlsanity.com/paper/2210.15872/) (2022)
- [Balanced Masked and Standard Face Recognition](https://mlsanity.com/paper/2110.01521/) (2021)
- [Confidence-Calibrated Face and Kinship Verification](https://mlsanity.com/paper/2210.13905/) (2022)
- [Semi-supervised Ranking for Object Image Blur Assessment](https://mlsanity.com/paper/2207.06085/) (2022)
- [Fair GANs through model rebalancing for extremely imbalanced class distributions](https://mlsanity.com/paper/2308.08638/) (2023)
- [Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)](https://mlsanity.com/paper/2609.02644/) (2026)

## Abstract (arXiv)

As the deep learning makes big progresses in still-image face recognition, unconstrained video face recognition is still a challenging task due to low quality face images caused by pose, blur, occlusion, illumination etc. In this paper we propose a network for face recognition which gives an explicit and quantitative quality score at the same time when a feature vector is extracted. To our knowledge this is the first network that implements these two functions in one network online. This network is very simple by adding a quality network branch to the baseline network of face recognition. It does not require training datasets with annotated face quality labels. We evaluate this network on both still-image face datasets and video face datasets and achieve the state-of-the-art performance in many cases. This network enables a lot of applications where an explicit face quality scpre is used. We demonstrate three applications of the explicit face quality, one of which is a progressive feature aggregation scheme in online video face recognition. We design an experiment to prove the benefits of using the face quality in this application. Code will be available at urlhttps://github.com/deepcam-cn/facequality.