EasyPortrait: Face Parsing and Portrait Segmentation Dataset

Karina Kvanchiani, Elizaveta Petrova, Karen Efremyan, Alexander Sautin, Alexander Kapitanov

2025

Abstract

Video conferencing apps have recently improved functionality by incorporating computer vision-based features such as real-time background removal and face beautification. The lack of diversity in existing portrait segmentation and face parsing datasets – particularly regarding head poses, ethnicity, scenes, and video conferencing-specific occlusions – motivated us to develop a new dataset, EasyPortrait, designed to address these tasks simultaneously. It contains 40,000 primarily indoor photos simulating video meeting scenarios, featuring 13,705 unique users and fine-grained segmentation masks divided into 9 classes. Since annotation masks from other datasets were unsuitable for our task, we revised the annotation guidelines, enabling EasyPortrait to handle cases like teeth whitening and skin smoothing. This paper also introduces a pipeline for data mining and high-quality mask annotation through crowdsourcing. The ablation study demonstrated the critical role of data quantity and head pose diversity in EasyPortrait. The cross-dataset evaluation experiments confirmed the best domain generalization ability among portrait segmentation datasets. The proposed dataset and trained models are publicly available.

Paper Citation

in Harvard Style

Kvanchiani K., Petrova E., Efremyan K., Sautin A. and Kapitanov A. (2025). EasyPortrait: Face Parsing and Portrait Segmentation Dataset. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 327-338. DOI: 10.5220/0013135300003912

in Bibtex Style

@conference{visapp25,
author={Karina Kvanchiani and Elizaveta Petrova and Karen Efremyan and Alexander Sautin and Alexander Kapitanov},
title={EasyPortrait: Face Parsing and Portrait Segmentation Dataset},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={327-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013135300003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - EasyPortrait: Face Parsing and Portrait Segmentation Dataset
SN - 978-989-758-728-3
AU - Kvanchiani K.
AU - Petrova E.
AU - Efremyan K.
AU - Sautin A.
AU - Kapitanov A.
PY - 2025
SP - 327
EP - 338
DO - 10.5220/0013135300003912
PB - SciTePress