EasyPortrait: Face Parsing and Portrait Segmentation Dataset

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

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 hea