Graph-based Facial Affect Analysis: A Review

TL;DR

This paper reviews the development and application of graph-based methods for facial affect analysis, highlighting recent advances with graph neural networks and discussing future challenges.

Contribution

It provides the first comprehensive survey of graph-based facial affect analysis methods, covering algorithm evolution, applications, and performance comparisons.

Findings

Graph-based FAA methods have evolved significantly over time.

Deep learning, especially graph neural networks, enhances FAA performance.

Current challenges include data quality and model interpretability.

Abstract

As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems. Early methods focus on extracting appearance and geometry features associated with human affects while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. This paper provides a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their non-deep or deep learning methods, emphasizing the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.