Welcome to the FAce Semantic SEGmentation (FASSEG) repository.

The FASSEG repository is composed by two datasets (frontal01 and frontal02) for frontal face segmentation, and one dataset (multipose01) with labaled faces in multiple poses.

If you use our datasets, please cite our works ([1] or [2], depending on the dataset).

Frontal01 contains 70 labeled frontal faces and the original RGB images. Original faces are mainly taken from the MIT-CBCL [3] and FEI [4] datasets.

This is the dataset we used in our work [1]. Images are organized in two folders - train and test - matching the division we adopted in the paper.

Frontal02 is an "high-precision 01". It contains the same images as Frontal01 but with much more precise segmentations.

Multipose01 contains more than 200 labeled faces in multiple poses. Original faces are taken from the Pointing04 database [5].

This is the dataset we used in our work [2]. Images are organized in two folders - train and test - matching the division we adopted in the paper.

Our advice is: if you need to compare with our results in [1], choose Frontal01. If you need a dataset to train a frontal face segmenter, choose Frontal02. If you are working with multiple head poses, choose Multipose01.

[1] Khalil Khan, Massimo Mauro, Riccardo Leonardi, "Multi-class semantic segmentation of faces", IEEE International Conference on Image Processing (ICIP), 2015 -- PDF

[2] Khalil Khan, Massimo Mauro, Pierangelo Migliorati, Riccardo Leonardi, "Head pose estimation through multiclass face segmentation", IEEE International Conference on Multimedia and Expo (ICME), 2017 In collaboration with YonderLabs -- PDF

[3] MIT Center for Biological and Computational Learning (CBCL), MIT-CBCL database, http://cbcl.mit.edu/software-datasets/FaceData2.html

[4] Centro Universitario da FEI, FEI database, http://www.fei.edu.br/~cet/facedatabase.html

[5] Nicolas Gourier, Daniela Hall, and James L Crowley, “Estimating face orientation from robust detection of salient facial structures” in FG Net Workshop on Visual Observation of Deictic Gestures. FGnet (IST– 2000–26434) Cambridge, UK, 2004, pp. 1–9