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Time Complexity for Face Recognition under varying Pose, Illumination and Facial Expressions based on Sparse Representation

by Steven Lawrence Fernandes, G. Josemin Bala
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 12
Year of Publication: 2012
Authors: Steven Lawrence Fernandes, G. Josemin Bala
10.5120/8092-1671

Steven Lawrence Fernandes, G. Josemin Bala . Time Complexity for Face Recognition under varying Pose, Illumination and Facial Expressions based on Sparse Representation. International Journal of Computer Applications. 51, 12 ( August 2012), 7-10. DOI=10.5120/8092-1671

@article{ 10.5120/8092-1671,
author = { Steven Lawrence Fernandes, G. Josemin Bala },
title = { Time Complexity for Face Recognition under varying Pose, Illumination and Facial Expressions based on Sparse Representation },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 12 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number12/8092-1671/ },
doi = { 10.5120/8092-1671 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:50:11.564580+05:30
%A Steven Lawrence Fernandes
%A G. Josemin Bala
%T Time Complexity for Face Recognition under varying Pose, Illumination and Facial Expressions based on Sparse Representation
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 12
%P 7-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sparse representation based face recognition is the most recent technique used, this technique first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. The l1-norm sparsity improves the face recognition accuracy. While most of the research focus has been in increasing the face recognition accuracy, in this paper we analyze the time needed for face recognition under varying Facial expressions, Pose and Illumination. This analysis is done on various public data sets. GRIMACE and ATT data sets provide variations in Facial expressions, SUBJECT data set provides Pose variations and YALEB data set provides 64 illumination conditions. The average time taken is calculated for each of the data set.

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Index Terms

Computer Science
Information Sciences

Keywords

Sparse representation Face recognition Facial expressions Pose variations Illumination conditions

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