Face images are subject to many different factors of variation, especially in unconstrained in-the-wild scenarios. For most tasks involving such images, e.g. expression recognition from video streams, having enough labeled data is prohibitively expensive. One common strategy to tackle such a problem is to learn disentangled representations for the different factors of variation of the observed data using adversarial learning. In this paper, we use a formulation of the adversarial loss to learn disentangled representations for face images. The used model facilitates learning on single-task datasets and improves the state-of-the-art in expression recognition with an accuracy of60.53%on the AffectNetdataset, without using any additional data.
@article{arxiv.2008.07001,
title = {Learning Disentangled Expression Representations from Facial Images},
author = {Marah Halawa and Manuel Wöllhaf and Eduardo Vellasques and Urko Sánchez Sanz and Olaf Hellwich},
journal= {arXiv preprint arXiv:2008.07001},
year = {2020}
}