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DOI: 10.14569/IJACSA.2018.091195
PDF

Empirical Evaluation of SVM for Facial Expression Recognition

Author 1: Saeeda Saeed
Author 2: Junaid Baber
Author 3: Maheen Bakhtyar
Author 4: Ihsan Ullah
Author 5: Naveed Sheikh
Author 6: Imam Dad
Author 7: Anwar Ali Sanjrani

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 11, 2018.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Support Vector Machines (SVMs) have shown bet-ter generalization and classification capabilities in different appli-cations of computer vision; SVM classifies underlying data by a hyperplane that can separate the two classes by maintaining the maximum margin between the support vectors of the respective classes. An empirical analysis of SVMs on the facial expression recognition task is reported with high intra and low inter class variations by conducting an extensive set of experiments on a large-scale Fer 2013 dataset. Three different kernel functions of SVM are used; linear kernel, quadratic kernel and cubic kernel, whereas, Histogram of Oriented Gradient (HoG) is used as a feature descriptor. Cubic Kernel achieves highest accuracy on Fer 2013 dataset using HoG.

Keywords: Facial Expression Recognition; Support Vector Ma-chine (SVM); Histogram of Oriented Gradients (HoG)

Saeeda Saeed, Junaid Baber, Maheen Bakhtyar, Ihsan Ullah, Naveed Sheikh, Imam Dad and Anwar Ali Sanjrani, “Empirical Evaluation of SVM for Facial Expression Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 9(11), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091195

@article{Saeed2018,
title = {Empirical Evaluation of SVM for Facial Expression Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091195},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091195},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Saeeda Saeed and Junaid Baber and Maheen Bakhtyar and Ihsan Ullah and Naveed Sheikh and Imam Dad and Anwar Ali Sanjrani}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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