Abstract
Traditional skin color detection technique has a weak anti-interference ability against the pixels with color similar to skin under a complex background, and cannot reduce the influence of illumination on the characteristics of skin color. In this study, an adaptive skin color detection method is proposed to tackle the is- sues. The skin section containing illumination information is extracted by combining the face detection methods proposed by Haar and Adaboost, and using the improved binarization algorithm. Then, combining the best threshold of luminance component (Y) of skin color samples obtained after training in the YCbCr space, the improved histogram backprojection method is adopted to detect the skin color of the whole image. Experiments show that the method is robust under complex background and the influence of illumination. Moreover, the method has a higher accuracy and recall rate than traditional skin color detection methods.
Highlights
Skin color detection is an important topic in image processing
After transforming the skin color pixels to the YCbCr color space, the components of Cb and Cr will cluster on a 2D plane in the shape similar to ellipse
Facial skin color pixels were used as the learning sample, and the Gaussian model or the weighted average Mahalanobis distance was adopted to judge whether other pixels are from skin or not
Summary
Skin color detection is an important topic in image processing. It makes contributions to various fields of computer vision such as face detection and recognition, gesture recognition, human-computer interaction and screening for objectionable images based on contents. To solve the problem of interference caused by complex background, adaptive skin color detection methods [3, 4, 5] have been proposed continually in recent years In these methods, facial skin color pixels were used as the learning sample, and the Gaussian model or the weighted average Mahalanobis distance was adopted to judge whether other pixels are from skin or not. The histogram backprojection algorithm of Cb-Cr component in the YCbCr color space is combined with the scope of luminance component (Y) to classify the extracted region In this way, the luminance information is taken into consideration, which contributes to the robustness of illumination and shadow, and decreases the missing rate. The skin section in an image can be detected accurately, which is a solution to the issue of interference caused by background similar to skin
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
