A face recognition software framework based on principal component analysis.

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition...

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Autores principales:	Peng Peng, Ivens Portugal, Paulo Alencar, Donald Cowan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/70ad7a7cec07416daf8d362c0c8bcf9b
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id	oai:doaj.org-article:70ad7a7cec07416daf8d362c0c8bcf9b
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spelling	oai:doaj.org-article:70ad7a7cec07416daf8d362c0c8bcf9b2021-12-02T20:06:35ZA face recognition software framework based on principal component analysis.1932-620310.1371/journal.pone.0254965https://doaj.org/article/70ad7a7cec07416daf8d362c0c8bcf9b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254965https://doaj.org/toc/1932-6203Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.Peng PengIvens PortugalPaulo AlencarDonald CowanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254965 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peng Peng Ivens Portugal Paulo Alencar Donald Cowan A face recognition software framework based on principal component analysis.
description	Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.
format	article
author	Peng Peng Ivens Portugal Paulo Alencar Donald Cowan
author_facet	Peng Peng Ivens Portugal Paulo Alencar Donald Cowan
author_sort	Peng Peng
title	A face recognition software framework based on principal component analysis.
title_short	A face recognition software framework based on principal component analysis.
title_full	A face recognition software framework based on principal component analysis.
title_fullStr	A face recognition software framework based on principal component analysis.
title_full_unstemmed	A face recognition software framework based on principal component analysis.
title_sort	face recognition software framework based on principal component analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/70ad7a7cec07416daf8d362c0c8bcf9b
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A face recognition software framework based on principal component analysis.

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