A Hybrid Skin Detection Model from Multiple Color Spaces Based on a Dual-Threshold Bayesian Algorithm

As a preliminary step of many applications, skin detection serves as an irreplaceable role in image processing applications, such as face recognition, gesture recognition, web image ̄ltering, and image retrieval systems. Combining information from multiple color spaces improves the recognition rate and reduces the error rate because the same color is represented di®erently in other color spaces. Consequently, a hybrid skin detection model from multiple color spaces based on a dual-threshold Bayesian algorithm (DTBA) has been proposed. In each color space, the pixels of images are divided into three categories, namely, skin, nonskin, andundetermined,whenusing the DTBA. Then, nearly all skin pixels are obtained by using a speci ̄c rule that combines the recognition results from multiple color spaces. Furthermore, skin texture ̄ltering and morphological ̄ltering are applied to the results by e®ectively reducing false identi ̄ed pixels. In addition, the proposed skin model can overcome interference from a complex background. The method has been validated in a series of experiments using theCompaq and the high-resolution image datasets (HRIDs). The ̄ndings have demonstrated the proposed approach produced an improvement, the true positive rate (TPR) improves more than 6% and the false positive rate (FPR) reduces more than 11%, compared with the Bayesian classi ̄er. We con ̄rm that the method is competitive. Meanwhile, this model is robust against skin distribution, scaling, partial occlusions, and illumination variations.