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Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation by Redondo, Rafael; Gibert, Jaume is a scholarly article available to read on EtoBox.

What is Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation about?

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.

Author

Redondo, Rafael; Gibert, Jaume

Published

2020

Language

EN