Model Based On Color And Texture Feature Of Skin Color Detection System Research

With the development of the skin color detection technology, automatic detection of skin in the identification and filtering of image content, video compression and content-based retrieval has played a very important role. And the skin detection technology based on color or texture has lot of advantages such as simple, fast, intuitive and not affected by morphological and viewpoint changes. The skin detection based on color and texture has important theoretical and application value, so it has been paid a lot of attentions by many scholars. In this paper a skin detection system based on color space and texture features was implemented, and the skin area can be detected more accurately using the system.In this paper, the skin detection technology was studied in details according to the color space model and the texture features.The main contributions are as follows:1、Some color models which are often used in skin detection, such as RGB、HSV、HIS and YcbCr were introduced and compared comprehensively.2、A skin detection model based on color space was proposed. First, the parameter values of the Gaussian model used in skin detection was learned from a large picture library, then the area of skin in the images was detected. A complete skin detection system was implemented. In the system, the Gaussian model, was used to detect the skin. The image illumination compensation and mean filtering technologies were used too. Simulation experiments for different color skins were introduced on MATLAB. The experimental results have shown that the effect of the system based on YCbCr space is better than the effect of the system based on the other color space and the algorithm is applicable to a wider range of lighting conditions. The proposed algorithm can detect the skin areas effectively from the color images with complex background.But the algorithm cannot avoid similar color regions being detected wrongly.3、A skin detection model based on the GLCM is proposed to improve the detection results of the color space model. All the work in this stage is based on the results of the work completed in the before stage. Images, were divided first to several image blocks whose central points are the skin pixels, and the size of the blocks is8×8. The parameter values of the skin detecting model was learned from a training skin library. Each pixel was identified using the skin model and the effective skin regions was determined in the images at last.The experimental results have shown that the proposed two-stage skin detection system based on color space and texture features can detect the skin color area more accurate and can effectively prevent similar color regions being detected wrongly.