# Masked Face Recognition Challenge: The WebFace260M Track Report

arXiv: https://arxiv.org/abs/2108.07189 · HTML: https://mlsanity.com/paper/2108.07189/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020)
- [The Devil of Face Recognition is in the Noise](https://mlsanity.com/paper/1807.11649/) (2018)
- [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020)
- [Level Playing Field for Million Scale Face Recognition](https://mlsanity.com/paper/1705.00393/) (2017)
- [UMDFaces: An Annotated Face Dataset for Training Deep Networks](https://mlsanity.com/paper/1611.01484/) (2016)
- [MFR 2021: Masked Face Recognition Competition](https://mlsanity.com/paper/2106.15288/) (2021)
- [Web-Scale Training for Face Identification](https://mlsanity.com/paper/1406.5266/) (2014)
- [Efficient Masked Face Recognition Method during the COVID-19 Pandemic](https://mlsanity.com/paper/2105.03026/) (2021)
- [iQIYI-VID: A Large Dataset for Multi-modal Person Identification](https://mlsanity.com/paper/1811.07548/) (2018)
- [Towards NIR-VIS Masked Face Recognition](https://mlsanity.com/paper/2104.06761/) (2021)

## Built on by (most cited first, 12 of 12)

- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Masked Face Recognition Challenge: The InsightFace Track Report](https://mlsanity.com/paper/2108.08191/) (2021)
- [MLFW: A Database for Face Recognition on Masked Faces](https://mlsanity.com/paper/2109.05804/) (2021)
- [Mask-invariant Face Recognition through Template-level Knowledge Distillation](https://mlsanity.com/paper/2112.05646/) (2021)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [A realistic approach to generate masked faces applied on two novel masked face recognition data sets](https://mlsanity.com/paper/2109.01745/) (2021)
- [Balanced Masked and Standard Face Recognition](https://mlsanity.com/paper/2110.01521/) (2021)
- [Towards Large-scale Masked Face Recognition](https://mlsanity.com/paper/2310.16364/) (2023)
- [ODNet: A Convolutional Neural Network for Asteroid Occultation Detection](https://mlsanity.com/paper/2210.16440/) (2022)
- [SASMU: boost the performance of generalized recognition model using synthetic face dataset](https://mlsanity.com/paper/2306.01449/) (2023)
- [Face Mask Removal with Region-attentive Face Inpainting](https://mlsanity.com/paper/2409.06845/) (2024)

## Abstract (arXiv)

According to WHO statistics, there are more than 204,617,027 confirmed COVID-19 cases including 4,323,247 deaths worldwide till August 12, 2021. During the coronavirus epidemic, almost everyone wears a facial mask. Traditionally, face recognition approaches process mostly non-occluded faces, which include primary facial features such as the eyes, nose, and mouth. Removing the mask for authentication in airports or laboratories will increase the risk of virus infection, posing a huge challenge to current face recognition systems. Due to the sudden outbreak of the epidemic, there are yet no publicly available real-world masked face recognition (MFR) benchmark. To cope with the above-mentioned issue, we organize the Face Bio-metrics under COVID Workshop and Masked Face Recognition Challenge in ICCV 2021. Enabled by the ultra-large-scale WebFace260M benchmark and the Face Recognition Under Inference Time conStraint (FRUITS) protocol, this challenge (WebFace260M Track) aims to push the frontiers of practical MFR. Since public evaluation sets are mostly saturated or contain noise, a new test set is gathered consisting of elaborated 2,478 celebrities and 60,926 faces. Meanwhile, we collect the world-largest real-world masked test set. In the first phase of WebFace260M Track, 69 teams (total 833 solutions) participate in the challenge and 49 teams exceed the performance of our baseline. There are second phase of the challenge till October 1, 2021 and on-going leaderboard. We will actively update this report in the future.