Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition

TL;DR

This paper introduces a multi-task learning framework using Graph Convolutional Networks to exploit dependencies between categorical and dimensional models of affect, improving facial expression recognition in-the-wild.

Contribution

It proposes a novel GCN-based multi-task learning approach that explicitly models dependencies between affect representations, enhancing FER performance.

Findings

Improves accuracy across multiple datasets and architectures.

Surpasses previous state-of-the-art on AffectNet.

Effectively captures emotional dependencies with GCNs.

Abstract

Over the past few years, deep learning methods have shown remarkable results in many face-related tasks including automatic facial expression recognition (FER) in-the-wild. Meanwhile, numerous models describing the human emotional states have been proposed by the psychology community. However, we have no clear evidence as to which representation is more appropriate and the majority of FER systems use either the categorical or the dimensional model of affect. Inspired by recent work in multi-label classification, this paper proposes a novel multi-task learning (MTL) framework that exploits the dependencies between these two models using a Graph Convolutional Network (GCN) to recognize facial expressions in-the-wild. Specifically, a shared feature representation is learned for both discrete and continuous recognition in a MTL setting. Moreover, the facial expression classifiers and the valence-arousal regressors are learned through a GCN that explicitly captures the dependencies between them. To evaluate the performance of our method under real-world conditions we perform extensive experiments on the AffectNet and Aff-Wild2 datasets. The results of our experiments show that our method is capable of improving the performance across different datasets and backbone architectures. Finally, we also surpass the previous state-of-the-art methods on the categorical model of AffectNet.