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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...

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Autores principales: Benini, Sergio, Khan, Khalil, Leonardi, Riccardo, Mauro, Massimo, Migliorati, Pierangelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
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).
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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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