Facial Expression Recognition with Machine Learning

Jia Xiu Chang*, Chin Poo Lee, Kian Ming Lim, Jit Yan Lim

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Human facial expressions play a crucial role in communication and enhancing interactions between humans and computers. This paper presents a novel approach for facial expression recognition using an ensemble classifier consisting of pre-trained models and vision transformers. The ensemble classifier comprises four models: VGG-19, VGGFace, ViT-B/16, and ViT-B/32. To evaluate the performance, the ensemble classifiers employ hard majority voting on three widely-used public datasets: CK+, FER2013, and JAFFE. The experimental results demonstrate that our proposed ensemble classifiers surpass the state-of-the-art methods across all datasets. Notably, we achieve outstanding accuracy rates, reaching 100% accuracy on the cleaned CK+ dataset, 76.30% accuracy on the cleaned FER-2013 dataset, and 100% accuracy on the cleaned JAFFE dataset.

Original languageEnglish
Title of host publication2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Pages125-130
Number of pages6
ISBN (Electronic)9798350321982
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Information and Communication Technology, ICoICT 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023

Publication series

Name2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Volume2023-August

Conference

Conference11th International Conference on Information and Communication Technology, ICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23

Keywords

  • Convolutional Neural Network (CNN)
  • Ensemble models
  • Facial expression recognition
  • Vision transformers

Fingerprint

Dive into the research topics of 'Facial Expression Recognition with Machine Learning'. Together they form a unique fingerprint.

Cite this