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Geometric Approach for Human Emotion Recognition using Facial Expression

by S. S. Bavkar, J. S. Rangole, V. U. Deshmukh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 14
Year of Publication: 2015
Authors: S. S. Bavkar, J. S. Rangole, V. U. Deshmukh
10.5120/20814-3174

S. S. Bavkar, J. S. Rangole, V. U. Deshmukh . Geometric Approach for Human Emotion Recognition using Facial Expression. International Journal of Computer Applications. 118, 14 ( May 2015), 17-22. DOI=10.5120/20814-3174

@article{ 10.5120/20814-3174,
author = { S. S. Bavkar, J. S. Rangole, V. U. Deshmukh },
title = { Geometric Approach for Human Emotion Recognition using Facial Expression },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 14 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number14/20814-3174/ },
doi = { 10.5120/20814-3174 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:01:42.549428+05:30
%A S. S. Bavkar
%A J. S. Rangole
%A V. U. Deshmukh
%T Geometric Approach for Human Emotion Recognition using Facial Expression
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 14
%P 17-22
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Paper contains emotion recognition system based on facial expression using Geometric approach. A human emotion recognition system consists of three steps: face detection, facial feature extraction and facial expression classification. In this paper, we used an anthropometric model to detect facial feature points. The detected feature points are group into two class static points and dynamic points. The distance between static points and dynamic points is used as a feature vector. Distance changes as we track these points in image sequence from neutral state to corresponding emotion. These distance vectors are used for input to classifier. SVM (Support Vector Machine) and RBFNN (Radial Basis Function Neural Network) used as classifier. Experimental results shows that the proposed approach is an effective method to recognize human emotions through facial expression with an emotion average recognition rate 91 % for experiment purpose the Cohn Kanade databases is used.

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Index Terms

Computer Science
Information Sciences

Keywords

Geometric Method Anthropometric model SVM RBFNN and LK Tracker

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