# Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation

arXiv: https://arxiv.org/abs/2006.13980 · HTML: https://mlsanity.com/paper/2006.13980/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets](https://mlsanity.com/paper/2205.06218/) (2022)
- [FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction](https://mlsanity.com/paper/2201.08425/) (2022)
- [Non-Deterministic Face Mask Removal Based On 3D Priors](https://mlsanity.com/paper/2202.09856/) (2022)

## Abstract (arXiv)

Existing face datasets often lack sufficient representation of occluding objects, which can hinder recognition, but also supply meaningful information to understand the visual context. In this work, we introduce Extended Labeled Faces in-the-Wild (ELFW), a dataset supplementing with additional face-related categories -- and also additional faces -- the originally released semantic labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset. Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking experiments, reveal that not only segmenting the augmented categories improves, but also the remaining ones benefit.