# End2End Occluded Face Recognition by Masking Corrupted Features

arXiv: https://arxiv.org/abs/2108.09468 · HTML: https://mlsanity.com/paper/2108.09468/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 21 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [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)
- [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)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network](https://mlsanity.com/paper/1908.06290/) (2019)
- [FaceX-Zoo: A PyTorch Toolbox for Face Recognition](https://mlsanity.com/paper/2101.04407/) (2021)
- [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020)
- [A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition](https://mlsanity.com/paper/1606.02894/) (2016)
- [Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes](https://mlsanity.com/paper/1405.1207/) (2014)
- [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild](https://mlsanity.com/paper/1612.08534/) (2016)

## Built on by (most cited first, 24 of 24)

- [Improving Vision Transformers by Revisiting High-frequency Components](https://mlsanity.com/paper/2204.00993/) (2022)
- [Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generation](https://mlsanity.com/paper/2307.09906/) (2023)
- [Triangle Attack: A Query-efficient Decision-based Adversarial Attack](https://mlsanity.com/paper/2112.06569/) (2021)
- [Diversifying the High-level Features for better Adversarial Transferability](https://mlsanity.com/paper/2304.10136/) (2023)
- [BackdoorBox: A Python Toolbox for Backdoor Learning](https://mlsanity.com/paper/2302.01762/) (2023)
- [Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips](https://mlsanity.com/paper/2207.13417/) (2022)
- [Controllable Inversion of Black-Box Face Recognition Models via Diffusion](https://mlsanity.com/paper/2303.13006/) (2023)
- [DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification](https://mlsanity.com/paper/2112.04016/) (2021)
- [Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data](https://mlsanity.com/paper/2412.01383/) (2024)
- [BATT: Backdoor Attack with Transformation-based Triggers](https://mlsanity.com/paper/2211.01806/) (2022)
- [Backdoor Defense via Suppressing Model Shortcuts](https://mlsanity.com/paper/2211.05631/) (2022)
- [ReliableSwap: Boosting General Face Swapping Via Reliable Supervision](https://mlsanity.com/paper/2306.05356/) (2023)
- [OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions](https://mlsanity.com/paper/2208.02760/) (2022)
- [Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers](https://mlsanity.com/paper/2311.02803/) (2023)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [Occlusion-Robust FAU Recognition by Mining Latent Space of Masked Autoencoders](https://mlsanity.com/paper/2212.04029/) (2022)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition](https://mlsanity.com/paper/2303.01309/) (2023)
- [Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition](https://mlsanity.com/paper/2311.11512/) (2023)
- [Mask-up: Investigating Biases in Face Re-identification for Masked Faces](https://mlsanity.com/paper/2402.13771/) (2024)
- [Robust Ensemble Person Re-Identification via Orthogonal Fusion with Occlusion Handling](https://mlsanity.com/paper/2404.00107/) (2024)
- [MoDE: Mixture of Diffusion Experts for Any Occluded Face Recognition](https://mlsanity.com/paper/2505.04306/) (2025)
- [CertDW: Towards Certified Dataset Ownership Verification via Conformal Calibration](https://mlsanity.com/paper/2506.13160/) (2025)
- [Video-Based Palm-Vein Authentication under Challenging Conditions](https://mlsanity.com/paper/2609.02776/) (2026)

## Abstract (arXiv)

With the recent advancement of deep convolutional neural networks, significant progress has been made in general face recognition. However, the state-of-the-art general face recognition models do not generalize well to occluded face images, which are exactly the common cases in real-world scenarios. The potential reasons are the absences of large-scale occluded face data for training and specific designs for tackling corrupted features brought by occlusions. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks. In addition, we construct massive occluded face images to train FROM effectively and efficiently. FROM is simple yet powerful compared to the existing methods that either rely on external detectors to discover the occlusions or employ shallow models which are less discriminative. Experimental results on the LFW, Megaface challenge 1, RMF2, AR dataset and other simulated occluded/masked datasets confirm that FROM dramatically improves the accuracy under occlusions, and generalizes well on general face recognition. Code is available at https://github.com/haibo-qiu/FROM