Learning morphological operators for skin detection

TL;DR

This paper introduces a new post-processing method using trained morphological operators to improve skin detection accuracy, validated across various datasets and detection approaches.

Contribution

It presents a novel approach that applies trained morphological operators as a post-processing step to enhance skin detection results.

Findings

Improves skin detection accuracy across multiple datasets.

Effective with both deep learning and handcrafted detection methods.

Validated through extensive experiments.

Abstract

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.