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.

本文提出了一种新的人体皮肤检测方法，该方法利用平滑的 2D 直方图和高斯模型相结合，结合眼部检测器对特定人员的皮肤模型进行优化，从而实现更准确、实用的肤色检测。通过在三个公共数据集上的定性和定量实验以及与现有方法的比较，结果表明该方法具有较好的效果和鲁棒性。