  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.
