Optical-Flow Based Symmetric Feature Extraction for Facial Expression Recognition

Authors

  • Mohammad Zeraatkar Department of Computer Engineering, Islamic Azad University, Tehran, Iran
  • Javad Joloudari Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran; Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol 3738147471, Iran; Department of Computer Engineering, Technical and Vocational University (TVU), Tehran
  • Kandala N. V. P. S. Rajesh School of Electronics Engineering, VIT-AP University, Vijayawada, India
  • Silvia Gaftandzhieva Faculty of Mathematics and Informatics, University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria
  • Sadiq Hussain Examination Branch, Dibrugarh University, Dibrugarh 786004, Assam, India

DOI:

https://doi.org/10.5755/j01.itc.54.1.36444

Keywords:

Facial Expression Recognition, Optical Flow Algorithm, Feature Extraction, Emotion Recognition, Extended Cohen-Kanade (CK ) Dataset

Abstract

Facial expression analysis is one of the most essential tools for behavior interpretation and emotion modeling in Intelligent Human-Computer Interaction (HCI). Although humans can easily interpret facial emotions, computers have great difficulty doing so. Analyzing changes and deformations in the face is one of the methods through which machines can interpret facial expressions. However, maintaining great precision while being accurate, stable, and quick is still challenging in this field. To address this issue, this research presents an innovative and novel method to fully automatically extract critical features from a face during a facial expression. Various machine learning models are used on these features to analyze emotions. We used the optical flow algorithm to extract motion vectors divided into sections on the subject’s face. Finally, each section and its symmetric section were used to calculate a new vector. The final features produce a state-of-the-art accuracy of over 98% in emotion classification in the Extended Cohen-Kanade (CK+) facial expression dataset. Furthermore, we proposed an algorithm to filter the most important features with an SVM classifier and achieved an accuracy of over 97 % by only looking at 15% of the face area.

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Published

2025-04-01

Issue

Section

Articles