Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation
This work addresses a domain-specific issue for face segmentation researchers by providing an incremental enhancement to a widely used dataset.
The paper tackles the problem of insufficient representation of occluding objects in face datasets by introducing the Extended Labeled Faces in-the-Wild (ELFW) dataset, which adds new face-related categories and uses data augmentation to improve segmentation performance for both augmented and existing categories.
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.