International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Facial Expressions Recognition Using Machine Learning Classifiers Based HOG Features

Abstract

Facial Expression Recognition (FER) is a critical area of research in computer vision and human-computer interaction. This paper presents a comprehensive study on the use of Histogram of Oriented Gradients (HOG) and machine learning algorithms for FER. We explore the effectiveness of HOG features in capturing facial expressions and evaluate the performance of various machine learning classifiers, including Support Vector Machines (SVM), Random Forests, and Neural Networks, in recognizing facial expressions. Our experiments are conducted on widely used JAFFE dataset. The results demonstrate that HOG features, when combined with SVM, achieve high accuracy in recognizing facial expressions, outperforming other feature extraction methods. This paper also discusses the challenges and future directions in FER systems, emphasizing the need for robust feature extraction and classification techniques in managing high-dimensional feature spaces and its appropriateness for facial expression recognition tasks. Note that SVM was used and we obtained results of 86%, and we used RF and obtained results of 81%. Future studies may focus on combining deep learning methods with hybrid feature extraction methods to improve performance on more complex datasets.

Country : Iraq

1 Mohammed Khalaf Fadel2 Mohammed Chachan Younis

  1. College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq
  2. College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq

IRJIET, Volume 9, Issue 5, May 2025 pp. 457-465

doi.org/10.47001/IRJIET/2025.905052

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