# Self-restrained Triplet Loss for Accurate Masked Face Recognition

arXiv: https://arxiv.org/abs/2103.01716 · HTML: https://mlsanity.com/paper/2103.01716/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [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)
- [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)
- [The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study](https://mlsanity.com/paper/2007.13521/) (2020)
- [Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes](https://mlsanity.com/paper/1405.1207/) (2014)
- [Grid Loss: Detecting Occluded Faces](https://mlsanity.com/paper/1609.00129/) (2016)
- [Triplet Distillation for Deep Face Recognition](https://mlsanity.com/paper/1905.04457/) (2019)

## Built on by (most cited first, 30 of 32)

- [SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data](https://mlsanity.com/paper/2206.10520/) (2022)
- [Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning](https://mlsanity.com/paper/2410.12952/) (2024)
- [MixFaceNets: Extremely Efficient Face Recognition Networks](https://mlsanity.com/paper/2107.13046/) (2021)
- [Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition](https://mlsanity.com/paper/2305.00358/) (2023)
- [Robust Physical-World Attacks on Face Recognition](https://mlsanity.com/paper/2109.09320/) (2021)
- [MFR 2021: Masked Face Recognition Competition](https://mlsanity.com/paper/2106.15288/) (2021)
- [Fairness in Face Presentation Attack Detection](https://mlsanity.com/paper/2209.09035/) (2022)
- [FocusFace: Multi-task Contrastive Learning for Masked Face Recognition](https://mlsanity.com/paper/2110.14940/) (2021)
- [Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection](https://mlsanity.com/paper/2111.04336/) (2021)
- [Consistency Regularization for Deep Face Anti-Spoofing](https://mlsanity.com/paper/2111.12320/) (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)
- [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)
- [Towards Explaining Demographic Bias through the Eyes of Face Recognition Models](https://mlsanity.com/paper/2208.13400/) (2022)
- [EFaR 2023: Efficient Face Recognition Competition](https://mlsanity.com/paper/2308.04168/) (2023)
- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [Octuplet Loss: Make Face Recognition Robust to Image Resolution](https://mlsanity.com/paper/2207.06726/) (2022)
- [A Survey on Drowsiness Detection -- Modern Applications and Methods](https://mlsanity.com/paper/2408.12990/) (2024)
- [Prototype Memory for Large-scale Face Representation Learning](https://mlsanity.com/paper/2105.02103/) (2021)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [Masked Faces with Faced Masks](https://mlsanity.com/paper/2201.06427/) (2022)
- [TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks](https://mlsanity.com/paper/2401.11598/) (2024)
- [SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation](https://mlsanity.com/paper/2312.12347/) (2023)
- [A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking](https://mlsanity.com/paper/2405.05900/) (2024)
- [Local and Global Feature Attention Fusion Network for Face Recognition](https://mlsanity.com/paper/2411.16169/) (2024)
- [Metric Learning as a Service with Covariance Embedding](https://mlsanity.com/paper/2211.15197/) (2022)
- [Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection](https://mlsanity.com/paper/2506.23469/) (2025)
- [Masked Face Recognition under Different Backbones](https://mlsanity.com/paper/2601.16440/) (2026)

## Abstract (arXiv)

Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms. After the current COVID-19 pandemic, wearing a face mask has been imposed in public places to keep the pandemic under control. However, face occlusion due to wearing a mask presents an emerging challenge for face recognition systems. In this paper, we present a solution to improve masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities. The achieved evaluation results on three face recognition models, two real masked datasets, and two synthetically generated masked face datasets proved that our proposed approach significantly improves the performance in most experimental settings.