Expression-identity Fusion Network for Facial Expression Recognition

Haifeng Zhang; Wen Su; Zengfu Wang

ICASSP 2019accepted0 citations

Expression-identity Fusion Network for Facial Expression Recognition

Abstract

Research shows that the facial expression recognition is strongly related to the person's identity. This paper presents an expression-identity fusion network to address the great inter-subject variations in facial expression recognition. The model is designed to jointly learn identity-related features and expression-related features via two branches with the same expression image input. A bilinear module is introduced to fuse two kinds of features and learn the relationship between them. Experimental results show that identity-related features can greatly boost the performance of facial expression recognition. Our method outperforms most of the state-of-the-art. On two popular facial expression databases (CK+ and Oulu-CASIA), our method achieves 96.02% and 85.21% recognition accuracy, respectively.

BibTeX

@inproceedings{icassp2019_expressionidenti,
  title = {Expression-identity Fusion Network for Facial Expression Recognition},
  author = {Haifeng Zhang and Wen Su and Zengfu Wang},
  booktitle = {ICASSP 2019},
  year = {2019}
}