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Recognition of Facial Expressions for Images using Neural Network

by Shubhangi Giripunje, Preeti Bajaj
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 11
Year of Publication: 2012
Authors: Shubhangi Giripunje, Preeti Bajaj
10.5120/5006-7324

Shubhangi Giripunje, Preeti Bajaj . Recognition of Facial Expressions for Images using Neural Network. International Journal of Computer Applications. 40, 11 ( December 2012), 3-7. DOI=10.5120/5006-7324

@article{ 10.5120/5006-7324,
author = { Shubhangi Giripunje, Preeti Bajaj },
title = { Recognition of Facial Expressions for Images using Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 40 },
number = { 11 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 3-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number11/5006-7324/ },
doi = { 10.5120/5006-7324 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:46.486976+05:30
%A Shubhangi Giripunje
%A Preeti Bajaj
%T Recognition of Facial Expressions for Images using Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 11
%P 3-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Globally terrorism continues to destroy the lives of people. To identify the terrorist amongst the other people is very difficult rather impossible. This exploratory study aims at investigating the effects of terrorism to recognize emotions. The current paper presents a view-based approach to the representation and recognition of human facial expression. In the designing of facial expression recognition (FER) system, Authors can take advantage of the resources and developed the algorithms for the system. The system divides into 3 modules, i.e. Preprocessing, Feature Extraction and Classification. The basis of the representation is a temporal template where the features are used typically based on local spatial position or displacement of specific points and regions of the face. In this paper, five different facial expressions were considered. Extracting the features, firstly logarithmic Gabor filters were applied. Then the Optimal subsets of features were selected for each expression. The classification tasks were performed using the Neural Network. Secondly, this study indicates that the YALE database contains expressers that expressed expressions.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Terrorism facial expression emotions Neural Network

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