# Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network

arXiv: https://arxiv.org/abs/1908.06290 · HTML: https://mlsanity.com/paper/1908.06290/

## Key facts

- Cited by 51 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 30 of 51)

- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021)
- [Accelerating DETR Convergence via Semantic-Aligned Matching](https://mlsanity.com/paper/2203.06883/) (2022)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Self-restrained Triplet Loss for Accurate Masked Face Recognition](https://mlsanity.com/paper/2103.01716/) (2021)
- [End2End Occluded Face Recognition by Masking Corrupted Features](https://mlsanity.com/paper/2108.09468/) (2021)
- [DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification](https://mlsanity.com/paper/2112.04016/) (2021)
- [Efficient Masked Face Recognition Method during the COVID-19 Pandemic](https://mlsanity.com/paper/2105.03026/) (2021)
- [LARNet: Lie Algebra Residual Network for Face Recognition](https://mlsanity.com/paper/2103.08147/) (2021)
- [FocusFace: Multi-task Contrastive Learning for Masked Face Recognition](https://mlsanity.com/paper/2110.14940/) (2021)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets](https://mlsanity.com/paper/2205.06218/) (2022)
- [Masked Face Recognition: Human vs. Machine](https://mlsanity.com/paper/2103.01924/) (2021)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [When Face Recognition Meets Occlusion: A New Benchmark](https://mlsanity.com/paper/2103.02805/) (2021)
- [Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping](https://mlsanity.com/paper/2104.00878/) (2021)
- [PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition](https://mlsanity.com/paper/2107.11721/) (2021)
- [Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion](https://mlsanity.com/paper/2207.14172/) (2022)
- [OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions](https://mlsanity.com/paper/2208.02760/) (2022)
- [Segmentation-Reconstruction-Guided Facial Image De-occlusion](https://mlsanity.com/paper/2112.08022/) (2021)
- [Depth as Attention for Face Representation Learning](https://mlsanity.com/paper/2101.00652/) (2021)
- [Locality-aware Channel-wise Dropout for Occluded Face Recognition](https://mlsanity.com/paper/2107.09270/) (2021)
- [OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks](https://mlsanity.com/paper/2301.11912/) (2023)
- [Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning](https://mlsanity.com/paper/2106.05596/) (2021)
- [HiMFR: A Hybrid Masked Face Recognition Through Face Inpainting](https://mlsanity.com/paper/2209.08930/) (2022)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [N-shot Palm Vein Verification Using Siamese Networks](https://mlsanity.com/paper/2109.12808/) (2021)
- [A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification](https://mlsanity.com/paper/2202.07358/) (2022)
- [Pairwise Learning via Stagewise Training in Proximal Setting](https://mlsanity.com/paper/2208.04075/) (2022)
- [Occlusion-Robust FAU Recognition by Mining Latent Space of Masked Autoencoders](https://mlsanity.com/paper/2212.04029/) (2022)

## Abstract (arXiv)

Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of the face recognition research in the past years. However, existing general CNN face models generalize poorly to the scenario of occlusions on variable facial areas. Inspired by the fact that a human visual system explicitly ignores occlusions and only focuses on non-occluded facial areas, we propose a mask learning strategy to find and discard the corrupted feature elements for face recognition. A mask dictionary is firstly established by exploiting the differences between the top convoluted features of occluded and occlusion-free face pairs using an innovatively designed Pairwise Differential Siamese Network (PDSN). Each item of this dictionary captures the correspondence between occluded facial areas and corrupted feature elements, which is named Feature Discarding Mask (FDM). When dealing with a face image with random partial occlusions, we generate its FDM by combining relevant dictionary items and then multiply it with the original features to eliminate those corrupted feature elements. Comprehensive experiments on both synthesized and realistic occluded face datasets show that the proposed approach significantly outperforms the state-of-the-arts.