# FocusFace: Multi-task Contrastive Learning for Masked Face Recognition arXiv: https://arxiv.org/abs/2110.14940 ยท HTML: https://mlsanity.com/paper/2110.14940/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 35 references that are in the map. - First posted on arXiv 28 Oct 2021. ## Builds on (most cited first, 23 of 23) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [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) - [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020) - [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021) - [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://mlsanity.com/paper/2004.00288/) (2020) - [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020) - [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021) - [Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network](https://mlsanity.com/paper/1908.06290/) (2019) - [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020) - [MixFaceNets: Extremely Efficient Face Recognition Networks](https://mlsanity.com/paper/2107.13046/) (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) - [MFR 2021: Masked Face Recognition Competition](https://mlsanity.com/paper/2106.15288/) (2021) - [Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection](https://mlsanity.com/paper/2103.01546/) (2021) - [Grid Loss: Detecting Occluded Faces](https://mlsanity.com/paper/1609.00129/) (2016) - [Boosting Masked Face Recognition with Multi-Task ArcFace](https://mlsanity.com/paper/2104.09874/) (2021) - [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021) - [Masked Face Recognition: Human vs. Machine](https://mlsanity.com/paper/2103.01924/) (2021) - [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) ## Built on by (most cited first, 12 of 12) - [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022) - [OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement](https://mlsanity.com/paper/2208.07841/) (2022) - [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022) - [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023) - [Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition](https://mlsanity.com/paper/2308.11840/) (2023) - [OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions](https://mlsanity.com/paper/2208.02760/) (2022) - [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022) - [MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition](https://mlsanity.com/paper/2408.16563/) (2024) - [Masked Student Dataset of Expressions](https://mlsanity.com/paper/2304.03867/) (2023) - [Localization using Multi-Focal Spatial Attention for Masked Face Recognition](https://mlsanity.com/paper/2305.01905/) (2023) - [SASMU: boost the performance of generalized recognition model using synthetic face dataset](https://mlsanity.com/paper/2306.01449/) (2023) - [Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis](https://mlsanity.com/paper/2501.07304/) (2025) ## Abstract (arXiv) SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a large number of countries. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. It has been shown that the performance of these methods drops considerably in the presence of face masks, especially if the reference image is unmasked. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition. The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks. We also explore different approaches to design the contrastive learning module. Results are presented in terms of masked-masked (M-M) and unmasked-masked (U-M) face verification performance. For both settings, the results are on par with published methods, but for M-M specifically, the proposed method was able to outperform all the solutions that it was compared to. We further show that when using our method on top of already existing methods the training computational costs decrease significantly while retaining similar performances. The implementation and the trained models are available at GitHub.