# MFR 2021: Masked Face Recognition Competition arXiv: https://arxiv.org/abs/2106.15288 ยท HTML: https://mlsanity.com/paper/2106.15288/ ## Key facts - Cited by 23 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 36 references that are in the map. - First posted on arXiv 29 Jun 2021. ## Builds on (most cited first, 18 of 18) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019) - [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014) - [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) - [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016) - [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018) - [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) - [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020) - [Fast and Accurate Model Scaling](https://mlsanity.com/paper/2103.06877/) (2021) - [Self-restrained Triplet Loss for Accurate Masked Face Recognition](https://mlsanity.com/paper/2103.01716/) (2021) - [The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study](https://mlsanity.com/paper/2007.13521/) (2020) - [Boosting Masked Face Recognition with Multi-Task ArcFace](https://mlsanity.com/paper/2104.09874/) (2021) - [AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces](https://mlsanity.com/paper/1706.04277/) (2017) - [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021) - [WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19](https://mlsanity.com/paper/2101.00784/) (2021) ## Built on by (most cited first, 23 of 23) - [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022) - [SDFR: Synthetic Data for Face Recognition Competition](https://mlsanity.com/paper/2404.04580/) (2024) - [FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration](https://mlsanity.com/paper/2212.02127/) (2022) - [A Quality Aware Sample-to-Sample Comparison for Face Recognition](https://mlsanity.com/paper/2306.04000/) (2023) - [On Recognizing Occluded Faces in the Wild](https://mlsanity.com/paper/2109.03672/) (2021) - [Masked Face Recognition Challenge: The WebFace260M Track Report](https://mlsanity.com/paper/2108.07189/) (2021) - [FocusFace: Multi-task Contrastive Learning for Masked Face Recognition](https://mlsanity.com/paper/2110.14940/) (2021) - [MLFW: A Database for Face Recognition on Masked Faces](https://mlsanity.com/paper/2109.05804/) (2021) - [Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection](https://mlsanity.com/paper/2111.04336/) (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) - [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022) - [My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition](https://mlsanity.com/paper/2108.00996/) (2021) - [The Effect of Wearing a Face Mask on Face Image Quality](https://mlsanity.com/paper/2110.11283/) (2021) - [OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions](https://mlsanity.com/paper/2208.02760/) (2022) - [EFaR 2023: Efficient Face Recognition Competition](https://mlsanity.com/paper/2308.04168/) (2023) - [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025) - [Fun Selfie Filters in Face Recognition: Impact Assessment and Removal](https://mlsanity.com/paper/2202.06022/) (2022) - [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022) - [Longitudinal Analysis of Mask and No-Mask on Child Face Recognition](https://mlsanity.com/paper/2111.00121/) (2021) - [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022) - [Does a Face Mask Protect my Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images](https://mlsanity.com/paper/2112.07879/) (2021) - [Masked Faces with Faced Masks](https://mlsanity.com/paper/2201.06427/) (2022) ## Abstract (arXiv) This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multi-session, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the top-performing academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.