Human Skin Detection Using RGB, HSV and YCbCr Color Models

TL;DR

This paper proposes a new human skin detection algorithm that combines RGB, HSV, and YCbCr color models to improve accuracy in recognizing skin pixels in images.

Contribution

The paper introduces a novel skin detection method that uses combined color model ranges for enhanced accuracy over existing techniques.

Findings

Improved skin pixel recognition accuracy.

Effective use of combined color model ranges.

Enhanced detection in varied lighting conditions.

Abstract

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.