New robust face recognition methods based on linear regression.

Nearest subspace (NS) classification based on linear regression technique is a very straightforward and efficient method for face recognition. A recently developed NS method, namely the linear regression-based classification (LRC), uses downsampled face images as features to perform face recognition...

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Autores principales:	Jian-Xun Mi, Jin-Xing Liu, Jiajun Wen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/583f1fe01cce40939f87ded300842b70
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spelling	oai:doaj.org-article:583f1fe01cce40939f87ded300842b702021-11-18T07:09:26ZNew robust face recognition methods based on linear regression.1932-620310.1371/journal.pone.0042461https://doaj.org/article/583f1fe01cce40939f87ded300842b702012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22879992/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Nearest subspace (NS) classification based on linear regression technique is a very straightforward and efficient method for face recognition. A recently developed NS method, namely the linear regression-based classification (LRC), uses downsampled face images as features to perform face recognition. The basic assumption behind this kind method is that samples from a certain class lie on their own class-specific subspace. Since there are only few training samples for each individual class, which will cause the small sample size (SSS) problem, this problem gives rise to misclassification of previous NS methods. In this paper, we propose two novel LRC methods using the idea that every class-specific subspace has its unique basis vectors. Thus, we consider that each class-specific subspace is spanned by two kinds of basis vectors which are the common basis vectors shared by many classes and the class-specific basis vectors owned by one class only. Based on this concept, two classification methods, namely robust LRC 1 and 2 (RLRC 1 and 2), are given to achieve more robust face recognition. Unlike some previous methods which need to extract class-specific basis vectors, the proposed methods are developed merely based on the existence of the class-specific basis vectors but without actually calculating them. Experiments on three well known face databases demonstrate very good performance of the new methods compared with other state-of-the-art methods.Jian-Xun MiJin-Xing LiuJiajun WenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e42461 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jian-Xun Mi Jin-Xing Liu Jiajun Wen New robust face recognition methods based on linear regression.
description	Nearest subspace (NS) classification based on linear regression technique is a very straightforward and efficient method for face recognition. A recently developed NS method, namely the linear regression-based classification (LRC), uses downsampled face images as features to perform face recognition. The basic assumption behind this kind method is that samples from a certain class lie on their own class-specific subspace. Since there are only few training samples for each individual class, which will cause the small sample size (SSS) problem, this problem gives rise to misclassification of previous NS methods. In this paper, we propose two novel LRC methods using the idea that every class-specific subspace has its unique basis vectors. Thus, we consider that each class-specific subspace is spanned by two kinds of basis vectors which are the common basis vectors shared by many classes and the class-specific basis vectors owned by one class only. Based on this concept, two classification methods, namely robust LRC 1 and 2 (RLRC 1 and 2), are given to achieve more robust face recognition. Unlike some previous methods which need to extract class-specific basis vectors, the proposed methods are developed merely based on the existence of the class-specific basis vectors but without actually calculating them. Experiments on three well known face databases demonstrate very good performance of the new methods compared with other state-of-the-art methods.
format	article
author	Jian-Xun Mi Jin-Xing Liu Jiajun Wen
author_facet	Jian-Xun Mi Jin-Xing Liu Jiajun Wen
author_sort	Jian-Xun Mi
title	New robust face recognition methods based on linear regression.
title_short	New robust face recognition methods based on linear regression.
title_full	New robust face recognition methods based on linear regression.
title_fullStr	New robust face recognition methods based on linear regression.
title_full_unstemmed	New robust face recognition methods based on linear regression.
title_sort	new robust face recognition methods based on linear regression.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/583f1fe01cce40939f87ded300842b70
work_keys_str_mv	AT jianxunmi newrobustfacerecognitionmethodsbasedonlinearregression AT jinxingliu newrobustfacerecognitionmethodsbasedonlinearregression AT jiajunwen newrobustfacerecognitionmethodsbasedonlinearregression
_version_	1718423852530270208

New robust face recognition methods based on linear regression.

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