  In this work we propose a novel post processing approach for skin detectors
based on trained morphological operators. The first step, consisting in skin
segmentation is performed according to an existing skin detection approach is
performed for skin segmentation, then a second step is carried out consisting
in the application of a set of morphological operators to refine the resulting
mask. Extensive experimental evaluation performed considering two different
detection approaches (one based on deep learning and a handcrafted one) carried
on 10 different datasets confirms the quality of the proposed method.
