Human Skin detection deals with the recognition of skin-colored pixels and
regions in a given image. Skin color is often used in human skin detection
because it is invariant to orientation and size and is fast to process. A new
human skin detection algorithm is proposed in this paper. The three main
parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue,
Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The
objective of proposed algorithm is to improve the recognition of skin pixels in
given images. The algorithm not only considers individual ranges of the three
color parameters but also takes into ac- count combinational ranges which
provide greater accuracy in recognizing the skin area in a given image.

本文提出了一种新的人类皮肤检测算法，该算法基于 RGB、HSV 和 YCbCr 颜色模型，通过考虑三个颜色参数的各自和联合范围来提高对给定图像中皮肤像素的识别精度。

利用 RGB、HSV 和 YCbCr 色彩模型进行人类皮肤检测

Human Skin Detection Using RGB, HSV and YCbCr Color Models

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。