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Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns
The localization of eye centers is a very useful cue for numerous applications like face recognition, facial expression recognition, and the early screening of neurological pathologies. Several methods relying on available light for accurate eye-center localization have been exploited. However, desp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4591357/ https://www.ncbi.nlm.nih.gov/pubmed/26426929 http://dx.doi.org/10.1371/journal.pone.0139098 |
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author | Pang, Zhiyong Wei, Chuansheng Teng, Dongdong Chen, Dihu Tan, Hongzhou |
author_facet | Pang, Zhiyong Wei, Chuansheng Teng, Dongdong Chen, Dihu Tan, Hongzhou |
author_sort | Pang, Zhiyong |
collection | PubMed |
description | The localization of eye centers is a very useful cue for numerous applications like face recognition, facial expression recognition, and the early screening of neurological pathologies. Several methods relying on available light for accurate eye-center localization have been exploited. However, despite the considerable improvements that eye-center localization systems have undergone in recent years, only few of these developments deal with the challenges posed by the profile (non-frontal face). In this paper, we first use the explicit shape regression method to obtain the rough location of the eye centers. Because this method extracts global information from the human face, it is robust against any changes in the eye region. We exploit this robustness and utilize it as a constraint. To locate the eye centers accurately, we employ isophote curvature features, the accuracy of which has been demonstrated in a previous study. By applying these features, we obtain a series of eye-center locations which are candidates for the actual position of the eye-center. Among these locations, the estimated locations which minimize the reconstruction error between the two methods mentioned above are taken as the closest approximation for the eye centers locations. Therefore, we combine explicit shape regression and isophote curvature feature analysis to achieve robustness and accuracy, respectively. In practical experiments, we use BioID and FERET datasets to test our approach to obtaining an accurate eye-center location while retaining robustness against changes in scale and pose. In addition, we apply our method to non-frontal faces to test its robustness and accuracy, which are essential in gaze estimation but have seldom been mentioned in previous works. Through extensive experimentation, we show that the proposed method can achieve a significant improvement in accuracy and robustness over state-of-the-art techniques, with our method ranking second in terms of accuracy. According to our implementation on a PC with a Xeon 2.5Ghz CPU, the frame rate of the eye tracking process can achieve 38 Hz. |
format | Online Article Text |
id | pubmed-4591357 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45913572015-10-09 Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns Pang, Zhiyong Wei, Chuansheng Teng, Dongdong Chen, Dihu Tan, Hongzhou PLoS One Research Article The localization of eye centers is a very useful cue for numerous applications like face recognition, facial expression recognition, and the early screening of neurological pathologies. Several methods relying on available light for accurate eye-center localization have been exploited. However, despite the considerable improvements that eye-center localization systems have undergone in recent years, only few of these developments deal with the challenges posed by the profile (non-frontal face). In this paper, we first use the explicit shape regression method to obtain the rough location of the eye centers. Because this method extracts global information from the human face, it is robust against any changes in the eye region. We exploit this robustness and utilize it as a constraint. To locate the eye centers accurately, we employ isophote curvature features, the accuracy of which has been demonstrated in a previous study. By applying these features, we obtain a series of eye-center locations which are candidates for the actual position of the eye-center. Among these locations, the estimated locations which minimize the reconstruction error between the two methods mentioned above are taken as the closest approximation for the eye centers locations. Therefore, we combine explicit shape regression and isophote curvature feature analysis to achieve robustness and accuracy, respectively. In practical experiments, we use BioID and FERET datasets to test our approach to obtaining an accurate eye-center location while retaining robustness against changes in scale and pose. In addition, we apply our method to non-frontal faces to test its robustness and accuracy, which are essential in gaze estimation but have seldom been mentioned in previous works. Through extensive experimentation, we show that the proposed method can achieve a significant improvement in accuracy and robustness over state-of-the-art techniques, with our method ranking second in terms of accuracy. According to our implementation on a PC with a Xeon 2.5Ghz CPU, the frame rate of the eye tracking process can achieve 38 Hz. Public Library of Science 2015-10-01 /pmc/articles/PMC4591357/ /pubmed/26426929 http://dx.doi.org/10.1371/journal.pone.0139098 Text en © 2015 Pang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Pang, Zhiyong Wei, Chuansheng Teng, Dongdong Chen, Dihu Tan, Hongzhou Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns |
title | Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns |
title_full | Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns |
title_fullStr | Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns |
title_full_unstemmed | Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns |
title_short | Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns |
title_sort | robust eye center localization through face alignment and invariant isocentric patterns |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4591357/ https://www.ncbi.nlm.nih.gov/pubmed/26426929 http://dx.doi.org/10.1371/journal.pone.0139098 |
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