Facial expression recognition in the wild based on convolutional neural network and graph convolutional network

Huimin Xiao; Xinghao Wang; Qiang Xing

doi:10.1117/12.3012353

21 December 2023 Facial expression recognition in the wild based on convolutional neural network and graph convolutional network

Huimin Xiao, Xinghao Wang, Qiang Xing

Author Affiliations +

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297042 (2023) https://doi.org/10.1117/12.3012353
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Facial Expression Recognition (FER) is a crucial research field, holding significant importance in understanding human emotions, communication, and social behavior. Although traditional FER methods perform well in laboratory settings, they often exhibit significantly reduced accuracy in the wild due to issues such as environmental noise, lighting variations, and individual visual differences, along with class-to-class and within-class variability in facial expressions. In response to these challenges, this paper proposes a method for FER in the wild based on Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs). The method first extracts facial expression features using CNN, fully leverages the relationship between facial expression samples to model them, and then uses GCN to learn the generated graph structure for classification purposes. The experimental outcomes demonstrate that our approach outperforms traditional methods in the task of FER.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huimin Xiao, Xinghao Wang, and Qiang Xing "Facial expression recognition in the wild based on convolutional neural network and graph convolutional network", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297042 (21 December 2023); https://doi.org/10.1117/12.3012353

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