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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Detalles Bibliográficos
Autores principales: Benini, Sergio, Khan, Khalil, Leonardi, Riccardo, Mauro, Massimo, Migliorati, Pierangelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Computer Science
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
Descripción
Sumario: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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