Learning Disentangled Expression Representations from Facial Images

TL;DR

This paper introduces an adversarial learning approach to obtain disentangled facial expression representations, enhancing expression recognition accuracy on AffectNet without extra data.

Contribution

It proposes a novel adversarial loss formulation for disentangling facial factors, improving state-of-the-art expression recognition performance.

Findings

Achieved 60.53% accuracy on AffectNet dataset

Enabled learning from single-task datasets

Improved expression recognition without additional data

Abstract

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.