Human Skin Detection Using RGB, HSV and YCbCr Color Models
This work addresses skin detection for applications like image processing and computer vision, but it appears incremental as it builds on existing color models without introducing a fundamentally new approach.
The paper tackled the problem of human skin detection in images by proposing a new algorithm that uses RGB, HSV, and YCbCr color models, aiming to improve recognition accuracy by considering both individual and combinational color ranges.
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.