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Facial Expression Recognition using PCA Algorithm

by Shweta Patil, S.S.Katariya
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 3
Year of Publication: 2015
Authors: Shweta Patil, S.S.Katariya

Shweta Patil, S.S.Katariya . Facial Expression Recognition using PCA Algorithm. Communications on Applied Electronics. 3, 3 ( October 2015), 22-24. DOI=10.5120/cae2015651904

@article{ 10.5120/cae2015651904,
author = { Shweta Patil, S.S.Katariya },
title = { Facial Expression Recognition using PCA Algorithm },
journal = { Communications on Applied Electronics },
issue_date = { October 2015 },
volume = { 3 },
number = { 3 },
month = { October },
year = { 2015 },
issn = { 2394-4714 },
pages = { 22-24 },
numpages = {9},
url = { },
doi = { 10.5120/cae2015651904 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:43:23.477947+05:30
%A Shweta Patil
%A S.S.Katariya
%T Facial Expression Recognition using PCA Algorithm
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 3
%P 22-24
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Face is the primary focus for the identity of human. But while detecting the face one difficulty is there. How to deal with the variations in the facial expressions, lightening etc. in this paper we use the principal component analysis (PCA) algorithm for the detection of facial expression. First the eigen spaces are created with the help of eigen vectors and eigen values. With the help of this space eigen faces are created and with the help of PCA algorithm the most matching eigen face is selected. The databases of 30 persons are generated each person having 10 photographs with different expressions like happy, angry, sad, neutral etc. If any expression is not recognize then it consider as a neutral expression. The classifier used are based on Euclidian distance. Train and test databases are there but that should be in similar conditions such as distance, lightening, background etc. The results shows the accuracy of this algorithm.

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Index Terms

Computer Science
Information Sciences


Principal component analysis Eigen vector Eigen values.