Unsupervised eye pupil localization through differential geometry and local self-similarity matching.

The automatic detection and tracking of human eyes and, in particular, the precise localization of their centers (pupils), is a widely debated topic in the international scientific community. In fact, the extracted information can be effectively used in a large number of applications ranging from ad...

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Autores principales:	Marco Leo, Dario Cazzato, Tommaso De Marco, Cosimo Distante
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c80919a3cef14fe080621d6c87f546f5
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spelling	oai:doaj.org-article:c80919a3cef14fe080621d6c87f546f52021-11-25T06:04:39ZUnsupervised eye pupil localization through differential geometry and local self-similarity matching.1932-620310.1371/journal.pone.0102829https://doaj.org/article/c80919a3cef14fe080621d6c87f546f52014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25122452/?tool=EBIhttps://doaj.org/toc/1932-6203The automatic detection and tracking of human eyes and, in particular, the precise localization of their centers (pupils), is a widely debated topic in the international scientific community. In fact, the extracted information can be effectively used in a large number of applications ranging from advanced interfaces to biometrics and including also the estimation of the gaze direction, the control of human attention and the early screening of neurological pathologies. Independently of the application domain, the detection and tracking of the eye centers are, currently, performed mainly using invasive devices. Cheaper and more versatile systems have been only recently introduced: they make use of image processing techniques working on periocular patches which can be specifically acquired or preliminarily cropped from facial images. In the latter cases the involved algorithms must work even in cases of non-ideal acquiring conditions (e.g in presence of noise, low spatial resolution, non-uniform lighting conditions, etc.) and without user's awareness (thus with possible variations of the eye in scale, rotation and/or translation). Getting satisfying results in pupils' localization in such a challenging operating conditions is still an open scientific topic in Computer Vision. Actually, the most performing solutions in the literature are, unfortunately, based on supervised machine learning algorithms which require initial sessions to set the working parameters and to train the embedded learning models of the eye: this way, experienced operators have to work on the system each time it is moved from an operational context to another. It follows that the use of unsupervised approaches is more and more desirable but, unfortunately, their performances are not still satisfactory and more investigations are required. To this end, this paper proposes a new unsupervised approach to automatically detect the center of the eye: its algorithmic core is a representation of the eye's shape that is obtained through a differential analysis of image intensities and the subsequent combination with the local variability of the appearance represented by self-similarity coefficients. The experimental evidence of the effectiveness of the method was demonstrated on challenging databases containing facial images. Moreover, its capabilities to accurately detect the centers of the eyes were also favourably compared with those of the leading state-of-the-art methods.Marco LeoDario CazzatoTommaso De MarcoCosimo DistantePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e102829 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marco Leo Dario Cazzato Tommaso De Marco Cosimo Distante Unsupervised eye pupil localization through differential geometry and local self-similarity matching.
description	The automatic detection and tracking of human eyes and, in particular, the precise localization of their centers (pupils), is a widely debated topic in the international scientific community. In fact, the extracted information can be effectively used in a large number of applications ranging from advanced interfaces to biometrics and including also the estimation of the gaze direction, the control of human attention and the early screening of neurological pathologies. Independently of the application domain, the detection and tracking of the eye centers are, currently, performed mainly using invasive devices. Cheaper and more versatile systems have been only recently introduced: they make use of image processing techniques working on periocular patches which can be specifically acquired or preliminarily cropped from facial images. In the latter cases the involved algorithms must work even in cases of non-ideal acquiring conditions (e.g in presence of noise, low spatial resolution, non-uniform lighting conditions, etc.) and without user's awareness (thus with possible variations of the eye in scale, rotation and/or translation). Getting satisfying results in pupils' localization in such a challenging operating conditions is still an open scientific topic in Computer Vision. Actually, the most performing solutions in the literature are, unfortunately, based on supervised machine learning algorithms which require initial sessions to set the working parameters and to train the embedded learning models of the eye: this way, experienced operators have to work on the system each time it is moved from an operational context to another. It follows that the use of unsupervised approaches is more and more desirable but, unfortunately, their performances are not still satisfactory and more investigations are required. To this end, this paper proposes a new unsupervised approach to automatically detect the center of the eye: its algorithmic core is a representation of the eye's shape that is obtained through a differential analysis of image intensities and the subsequent combination with the local variability of the appearance represented by self-similarity coefficients. The experimental evidence of the effectiveness of the method was demonstrated on challenging databases containing facial images. Moreover, its capabilities to accurately detect the centers of the eyes were also favourably compared with those of the leading state-of-the-art methods.
format	article
author	Marco Leo Dario Cazzato Tommaso De Marco Cosimo Distante
author_facet	Marco Leo Dario Cazzato Tommaso De Marco Cosimo Distante
author_sort	Marco Leo
title	Unsupervised eye pupil localization through differential geometry and local self-similarity matching.
title_short	Unsupervised eye pupil localization through differential geometry and local self-similarity matching.
title_full	Unsupervised eye pupil localization through differential geometry and local self-similarity matching.
title_fullStr	Unsupervised eye pupil localization through differential geometry and local self-similarity matching.
title_full_unstemmed	Unsupervised eye pupil localization through differential geometry and local self-similarity matching.
title_sort	unsupervised eye pupil localization through differential geometry and local self-similarity matching.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/c80919a3cef14fe080621d6c87f546f5
work_keys_str_mv	AT marcoleo unsupervisedeyepupillocalizationthroughdifferentialgeometryandlocalselfsimilaritymatching AT dariocazzato unsupervisedeyepupillocalizationthroughdifferentialgeometryandlocalselfsimilaritymatching AT tommasodemarco unsupervisedeyepupillocalizationthroughdifferentialgeometryandlocalselfsimilaritymatching AT cosimodistante unsupervisedeyepupillocalizationthroughdifferentialgeometryandlocalselfsimilaritymatching
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Unsupervised eye pupil localization through differential geometry and local self-similarity matching.

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