A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm

Skin detection is an interesting problem in image processing and is an important preprocessing step for further techniques like face detection, objectionable image detection, etc. However, its performance has not really been high because of the high overlapped degree between “skin” and “nonskin” pixels. This paper proposes a new approach to improve the skin detection performance using the Bayesian classifier and connected component algorithm. Specifically, the Bayesian classifier is utilized to identify “true skin” pixels using the first posterior probability threshold, which is approximate to 1, and to identify "skin candidate" pixels using the second posterior probability threshold. Subsequently, the connected component algorithm is used to find all the connected components containing the “skin candidate” pixels. According to the fact that a skin pixel often connects with other skin pixels in an image, all pixels in a connected component are classified as “skin” if there is at least one “true skin” pixel in that connected component. It means that the “nonskin” pixels whose color is similar to skin are classified as “nonskin” when they have the posterior probabilities lower than the first posterior probability threshold and do not connect with any “true skin” pixel. This idea can help us to improve the skin classification performance, especially the false positive rate.