Empirical Evaluation of SVM for Facial Expression Recognition

被引：0

作者：

Saeed, Saeeda ^{[1
]}

Baber, Junaid ^{[1
]}

Bakhtyar, Maheen ^{[1
]}

Ullah, Ihsan ^{[1
]}

Sheikh, Naveed ^{[2
]}

Dad, Imam ^{[1
]}

Sanjrani, Anwar Ali ^{[1
]}

机构：

[1] Univ Balochistan, Dept Comp Sci & IT, Quetta, Balochistan, Pakistan

[2] Univ Balochistan, Dept Math, Quetta, Balochistan, Pakistan

来源：

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2018年 / 9卷 / 11期

关键词：

Facial Expression Recognition; Support Vector Machine (SVM); Histogram of Oriented Gradients (HoG);

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Support Vector Machines (SVMs) have shown better generalization and classification capabilities in different applications 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.

引用

页码：670 / 673

页数：4

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