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FASSEG: A FAce semantic SEGmentation repository for face image analysis
The FASSEG repository is composed by four subsets containing face images useful for training and testing automatic methods for the task of face segmentation. Threesubsets, namely frontal01, frontal02, and frontal03 are specifically built for performing frontal face segmentation. Frontal01 contains 7...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454221/ https://www.ncbi.nlm.nih.gov/pubmed/31008162 http://dx.doi.org/10.1016/j.dib.2019.103881 |
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author | Benini, Sergio Khan, Khalil Leonardi, Riccardo Mauro, Massimo Migliorati, Pierangelo |
author_facet | Benini, Sergio Khan, Khalil Leonardi, Riccardo Mauro, Massimo Migliorati, Pierangelo |
author_sort | Benini, Sergio |
collection | PubMed |
description | The FASSEG repository is composed by four subsets containing face images useful for training and testing automatic methods for the task of face segmentation. Threesubsets, namely frontal01, frontal02, and frontal03 are specifically built for performing frontal face segmentation. Frontal01 contains 70 original RGB images and the corresponding roughly labelledground-truth masks. Frontal02 contains the same image data, with high-precision labelled ground-truth masks. Frontal03 consists in 150 annotated face masks of twins captured in various orientations, illumination conditions and facial expressions. The last subset, namely multipose01, contains more than 200 faces in multiple poses and the corresponding ground-truth masks. For all face images, ground-truth masks are labelled on six classes (mouth, nose, eyes, hair, skin, and background). |
format | Online Article Text |
id | pubmed-6454221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-64542212019-04-19 FASSEG: A FAce semantic SEGmentation repository for face image analysis Benini, Sergio Khan, Khalil Leonardi, Riccardo Mauro, Massimo Migliorati, Pierangelo Data Brief Computer Science The FASSEG repository is composed by four subsets containing face images useful for training and testing automatic methods for the task of face segmentation. Threesubsets, namely frontal01, frontal02, and frontal03 are specifically built for performing frontal face segmentation. Frontal01 contains 70 original RGB images and the corresponding roughly labelledground-truth masks. Frontal02 contains the same image data, with high-precision labelled ground-truth masks. Frontal03 consists in 150 annotated face masks of twins captured in various orientations, illumination conditions and facial expressions. The last subset, namely multipose01, contains more than 200 faces in multiple poses and the corresponding ground-truth masks. For all face images, ground-truth masks are labelled on six classes (mouth, nose, eyes, hair, skin, and background). Elsevier 2019-03-29 /pmc/articles/PMC6454221/ /pubmed/31008162 http://dx.doi.org/10.1016/j.dib.2019.103881 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Computer Science Benini, Sergio Khan, Khalil Leonardi, Riccardo Mauro, Massimo Migliorati, Pierangelo FASSEG: A FAce semantic SEGmentation repository for face image analysis |
title | FASSEG: A FAce semantic SEGmentation repository for face image analysis |
title_full | FASSEG: A FAce semantic SEGmentation repository for face image analysis |
title_fullStr | FASSEG: A FAce semantic SEGmentation repository for face image analysis |
title_full_unstemmed | FASSEG: A FAce semantic SEGmentation repository for face image analysis |
title_short | FASSEG: A FAce semantic SEGmentation repository for face image analysis |
title_sort | fasseg: a face semantic segmentation repository for face image analysis |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454221/ https://www.ncbi.nlm.nih.gov/pubmed/31008162 http://dx.doi.org/10.1016/j.dib.2019.103881 |
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