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Automatic Classification of Facial Expressions from Video Stream using Decision Tree

by Bali Thorat, Ganesh Manza, Pravin Yannawar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 22
Year of Publication: 2015
Authors: Bali Thorat, Ganesh Manza, Pravin Yannawar
10.5120/21835-5096

Bali Thorat, Ganesh Manza, Pravin Yannawar . Automatic Classification of Facial Expressions from Video Stream using Decision Tree. International Journal of Computer Applications. 121, 22 ( July 2015), 32-36. DOI=10.5120/21835-5096

@article{ 10.5120/21835-5096,
author = { Bali Thorat, Ganesh Manza, Pravin Yannawar },
title = { Automatic Classification of Facial Expressions from Video Stream using Decision Tree },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 22 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number22/21835-5096/ },
doi = { 10.5120/21835-5096 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:10.042435+05:30
%A Bali Thorat
%A Ganesh Manza
%A Pravin Yannawar
%T Automatic Classification of Facial Expressions from Video Stream using Decision Tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 22
%P 32-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Facial expression is one of the most powerful, natural, and immediate means for human beings to communicate their emotions. This paper presents automatic recognition system for Happy, Surprise, Disgust, Sad, Anger and Fear facial expressions contained in video streams using decision tree. The proposed method employs popular and updated 'Viola-Jones' detection method to detect the face, facial components and their classification using decision tree. This research work attempts to recognize fine-grained changes in facial expression and established their relationship with Facial Action Coding System (FACS). The proposed method resulted in average 76. 43% correct classification of six basic expressions from video streams with 23. 56% expression error rate.

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Index Terms

Computer Science
Information Sciences

Keywords

Face Detection Facial feature point extraction Facial expression recognition Feature extraction Decision Tree.

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