Multi-Class Semantic Segmentation of Faces

In this paper the problem of multi-class face segmentation is introduced. Differently from previous works which only consider few classes - typically skin and hair - the label set is extended here to six categories: skin, hair, eyes, nose, mouth and background. A dataset with 70 images taken from MIT-CBCL and FEI face databases is manually annotated and made publicly available1. Three kind of local features - accounting for color, shape and location - are extracted from uniformly sampled square patches. A discriminative model is built with random decision forests and used for classification. Many different combinations of features and parameters are explored to find the best possible model configuration. Our analysis shows that very good performance (~ 93% in accuracy) can be achieved with a fairly simple model.