Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition

TL;DR

Exp-Graph introduces a graph-based framework that models facial attribute relationships using landmarks and vision transformers, significantly improving facial expression recognition accuracy across multiple datasets.

Contribution

The paper presents a novel graph-based approach combining facial landmarks, vision transformers, and graph convolutional networks for enhanced expression recognition.

Findings

Achieved high accuracy on Oulu-CASIA with 98.09%

Demonstrated strong generalization on AFEW with 56.39%

Effectively captures local and global facial attribute dependencies

Abstract

Facial expression recognition is crucial for human-computer interaction applications such as face animation, video surveillance, affective computing, medical analysis, etc. Since the structure of facial attributes varies with facial expressions, incorporating structural information into facial attributes is essential for facial expression recognition. In this paper, we propose Exp-Graph, a novel framework designed to represent the structural relationships among facial attributes using graph-based modeling for facial expression recognition. For facial attributes graph representation, facial landmarks are used as the graph's vertices. At the same time, the edges are determined based on the proximity of the facial landmark and the similarity of the local appearance of the facial attributes encoded using the vision transformer. Additionally, graph convolutional networks are utilized to capture and integrate these structural dependencies into the encoding of facial attributes, thereby enhancing the accuracy of expression recognition. Thus, Exp-Graph learns from the facial attribute graphs highly expressive semantic representations. On the other hand, the vision transformer and graph convolutional blocks help the framework exploit the local and global dependencies among the facial attributes that are essential for the recognition of facial expressions. We conducted comprehensive evaluations of the proposed Exp-Graph model on three benchmark datasets: Oulu-CASIA, eNTERFACE05, and AFEW. The model achieved recognition accuracies of 98.09\%, 79.01\%, and 56.39\%, respectively. These results indicate that Exp-Graph maintains strong generalization capabilities across both controlled laboratory settings and real-world, unconstrained environments, underscoring its effectiveness for practical facial expression recognition applications.