# FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction

arXiv: https://arxiv.org/abs/2201.08425 · HTML: https://mlsanity.com/paper/2201.08425/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Towards Fast, Accurate and Stable 3D Dense Face Alignment](https://mlsanity.com/paper/2009.09960/) (2020)
- [On Face Segmentation, Face Swapping, and Face Perception](https://mlsanity.com/paper/1704.06729/) (2017)
- [Real-Time Facial Segmentation and Performance Capture from RGB Input](https://mlsanity.com/paper/1604.02647/) (2016)
- [ByeGlassesGAN: Identity Preserving Eyeglasses Removal for Face Images](https://mlsanity.com/paper/2008.11042/) (2020)
- [Towards Learning Structure via Consensus for Face Segmentation and Parsing](https://mlsanity.com/paper/1911.00957/) (2019)
- [Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation](https://mlsanity.com/paper/2006.13980/) (2020)

## Built on by (most cited first, 4 of 4)

- [Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets](https://mlsanity.com/paper/2205.06218/) (2022)
- [EasyPortrait -- Face Parsing and Portrait Segmentation Dataset](https://mlsanity.com/paper/2304.13509/) (2023)
- [Eye Sclera for Fair Face Image Quality Assessment](https://mlsanity.com/paper/2501.07158/) (2025)
- [Uncertainty-Guided Face Matting for Occlusion-Aware Face Transformation](https://mlsanity.com/paper/2508.03055/) (2025)

## Abstract (arXiv)

Occlusions often occur in face images in the wild, troubling face-related tasks such as landmark detection, 3D reconstruction, and face recognition. It is beneficial to extract face regions from unconstrained face images accurately. However, current face segmentation datasets suffer from small data volumes, few occlusion types, low resolution, and imprecise annotation, limiting the performance of data-driven-based algorithms. This paper proposes a novel face occlusion dataset with manually labeled face occlusions from the CelebA-HQ and the internet. The occlusion types cover sunglasses, spectacles, hands, masks, scarfs, microphones, etc. To the best of our knowledge, it is by far the largest and most comprehensive face occlusion dataset. Combining it with the attribute mask in CelebAMask-HQ, we trained a straightforward face segmentation model but obtained SOTA performance, convincingly demonstrating the effectiveness of the proposed dataset.