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Reseach Article

Emotion Detection from Facial Expression using Support Vector Machine

by Vanita Jain, Pratiksha Aggarwal, Tarun Kumar, Vaibhav Taneja
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 8
Year of Publication: 2017
Authors: Vanita Jain, Pratiksha Aggarwal, Tarun Kumar, Vaibhav Taneja
10.5120/ijca2017914398

Vanita Jain, Pratiksha Aggarwal, Tarun Kumar, Vaibhav Taneja . Emotion Detection from Facial Expression using Support Vector Machine. International Journal of Computer Applications. 167, 8 ( Jun 2017), 25-28. DOI=10.5120/ijca2017914398

@article{ 10.5120/ijca2017914398,
author = { Vanita Jain, Pratiksha Aggarwal, Tarun Kumar, Vaibhav Taneja },
title = { Emotion Detection from Facial Expression using Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 8 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number8/27794-2017914398/ },
doi = { 10.5120/ijca2017914398 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:19.411667+05:30
%A Vanita Jain
%A Pratiksha Aggarwal
%A Tarun Kumar
%A Vaibhav Taneja
%T Emotion Detection from Facial Expression using Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 8
%P 25-28
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this paper is to apply Support Vector Machine to the problem of classifying emotion on images of human faces. This well defined problem is complicated by the natural variation in people’s faces, requiring the classification algorithm to distinguish the small number of relevant features from the large pool of input features. Three different kernels i.e., linear kernel, polynomial kernel and RBF kernel are used to recognise eight facial expressions, anger, contempt, disgust, fear, happiness, neutral, sadness and surprise of human beings in still images. Accuracy of the three kernels is compared to judge the best kernel for facial expression recognition.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Facial Expression Support Vector Machine Emotion Detection