Empirical Evaluation of SVM for Facial Expression Recognition
Saeeda Saeed, Junaid Baber, Maheen Bakhtyar, Ihsan Ullah, Naveed Sheikh, Imam Dad, Anwar Ali · International Journal of Advanced Computer Science and Applications · 2018
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.