A Fusion Approach for Efficient Human Skin Detection

TL;DR

This paper introduces a novel fusion-based human skin detection method combining a smoothed 2D histogram and Gaussian model, which is adaptable, accurate, and computationally efficient across diverse ethnicities and lighting conditions.

Contribution

It presents the first fusion strategy for human skin detection, reducing computational costs and improving accuracy without requiring training.

Findings

Effective on three public datasets

Outperforms state-of-the-art methods

Robust across different ethnicities and illumination

Abstract

A reliable human skin detection method that is adaptable to different human skin colours and illu- mination conditions is essential for better human skin segmentation. Even though different human skin colour detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colours across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin de- tection approach that combines a smoothed 2D histogram and Gaussian model, for automatic human skin detection in colour image(s). In our approach an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required; and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.