Robust skin detector based on AdaBoost and statistical luminance features

Skin detection is one of the most important targets of image processing and computer vision. One big concern about skin detection algorithms is their simplicity while keeping a good accuracy in discriminating skin and non-skin pixels. This paper presents a novel and robust skin detector. In this study, statistical information of each pixel and its neighbours were taken into account in order to deal with this concern. In proposed method, a cascaded classifier using AdaBoost algorithm was trained. Its errors were found and corrected. Finally, two edge detectors were used to make the algorithm more accurate. Also, some small tricks were used to make the whole process faster. The performances of proposed skin detector were evaluated using SFA skin database. Eventually, the method was compared with some popular and newly established skin detectors. The experimental results show that the proposed scheme outperforms other skin detection methods due to high precision and good recall.