Integration of 2D Textural and 3D Geometric Features for Robust Facial Expression Recognition

Authors

  • Fouzia Adjailia Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice, Slovak Republic
  • Messaoud Ramdani Laboratory of Automation and Signals (LASA), Faculty of Engineering, University Badji, Mokhtar of Annaba, Annaba, 23000, Algeria
  • Andrinandrasana David Rasamoelina Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice, Slovak Republic
  • Peter Sincak Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice, Slovakia & Institute of Informatics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary

DOI:

https://doi.org/10.31577/cai_2021_5_988

Keywords:

Facial expression recognition, histogram of oriented gradient, local binary pattern, descriptors, feature extraction, voxels

Abstract

Recognition of facial expressions is critical for successful social interactions and relationships. Facial expressions transmit emotional information, which is critical for human-machine interaction; therefore, significant research in computer vision has been conducted, with promising findings in using facial expression detection in both academia and industry. 3D pictures acquired enormous popularity owing to their ability to overcome some of the constraints inherent in 2D imagery, such as lighting and variation. We present a method for recognizing facial expressions in this article by combining features extracted from 2D textured pictures and 3D geometric data using the Local Binary Pattern (LBP) and the 3D Voxel Histogram of Oriented Gradients (3DVHOG), respectively. We performed various pre-processing operations using the MDPA-FACE3D and Bosphorus datasets, then we carried out classification process to classify images into seven universal emotions, namely anger, disgust, fear, happiness, sadness, neutral, and surprise. Using Support Vector Machine classifier, we achieved the accuracy of 88.5 % and 92.9 % on the MDPA-FACE3D and the Bosphorus datasets, respectively.

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Published

2021-12-31

How to Cite

Adjailia, F., Ramdani, . M., Rasamoelina, . A. D., & Sincak, P. . (2021). Integration of 2D Textural and 3D Geometric Features for Robust Facial Expression Recognition. COMPUTING AND INFORMATICS, 40(5), 988–1007. https://doi.org/10.31577/cai_2021_5_988