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Reseach Article

Implementation and Comparison of Facial Expression Detection and Classification Techniques

by Anupam Tripathi, Nikhil Thakurdesai
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 18
Year of Publication: 2018
Authors: Anupam Tripathi, Nikhil Thakurdesai
10.5120/ijca2018917893

Anupam Tripathi, Nikhil Thakurdesai . Implementation and Comparison of Facial Expression Detection and Classification Techniques. International Journal of Computer Applications. 182, 18 ( Sep 2018), 25-29. DOI=10.5120/ijca2018917893

@article{ 10.5120/ijca2018917893,
author = { Anupam Tripathi, Nikhil Thakurdesai },
title = { Implementation and Comparison of Facial Expression Detection and Classification Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 182 },
number = { 18 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number18/29978-2018917893/ },
doi = { 10.5120/ijca2018917893 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:47.225454+05:30
%A Anupam Tripathi
%A Nikhil Thakurdesai
%T Implementation and Comparison of Facial Expression Detection and Classification Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 18
%P 25-29
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Facial expressions are one of the most important behavioral measures for emotion recognition. Expressions can tell a lot about the person, his behavior, what he is thinking and this data is vital in making various predictions which can have a variety of applications. In this paper we have implemented and compared three types of facial expression recognition and classification techniques. The first one is a state-of-the-art convolutional neural network, the second one is a transfer learning approach using the InceptionV3 model and in the last one, we have extracted the 68 facial points which have been identified as important for recognizing the expression of a person and passed it to a deep neural network. All these techniques have given accuracies over 90%, so comes the need to compare them in detail and determine which one of them would give results more accurately and efficiently.

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Index Terms

Computer Science
Information Sciences

Keywords

Facial Expression Recognition CNN Transfer learning Haar Cascades