EasyPortrait -- Face Parsing and Portrait Segmentation Dataset
This dataset addresses the need for more diverse and high-quality training data for video conferencing applications like background removal and face beautification, though it is incremental as it builds on existing dataset efforts.
The authors tackled the problem of limited variability in existing datasets for face parsing and portrait segmentation by creating EasyPortrait, a new dataset with 40,000 indoor photos and 13,705 unique users, which demonstrated the best domain generalization ability among portrait segmentation datasets in cross-dataset evaluations.
Recently, video conferencing apps have become functional by accomplishing such computer vision-based features as real-time background removal and face beautification. Limited variability in existing portrait segmentation and face parsing datasets, including head poses, ethnicity, scenes, and occlusions specific to video conferencing, motivated us to create a new dataset, EasyPortrait, for these tasks simultaneously. It contains 40,000 primarily indoor photos repeating video meeting scenarios with 13,705 unique users and fine-grained segmentation masks separated into 9 classes. Inappropriate annotation masks from other datasets caused a revision of annotator guidelines, resulting in EasyPortrait's ability to process cases, such as teeth whitening and skin smoothing. The pipeline for data mining and high-quality mask annotation via crowdsourcing is also proposed in this paper. In the ablation study experiments, we proved the importance of data quantity and diversity in head poses in our dataset for the effective learning of the model. The cross-dataset evaluation experiments confirmed the best domain generalization ability among portrait segmentation datasets. Moreover, we demonstrate the simplicity of training segmentation models on EasyPortrait without extra training tricks. The proposed dataset and trained models are publicly available.