Robust cascaded skin detector based on AdaBoost

Skin detection is one of the most important issues of image processing and computer vision. A big concern about skin detection algorithms is being simple while keeping a good accuracy in discriminating skin and non-skin pixels to make the skin detector robust and practicable. This paper proposes a novel and robust skin detector. In this work, statistical information of each pixel and its neighbors is taken into account in order to deal with this concern. A simple algorithm is also presented to reduce the computational complexity of computing the statistical information. In the proposed method, a cascaded classifier by using the AdaBoost algorithm is trained. Finally, two edge detectors are used to make the algorithm more accurate. Moreover, some simple algorithms are used to make the process faster (e.g. algorithms of calculating mean and variance). The performance of the proposed skin detector is evaluated using SFA skin database. In order to illustrate the robustness of the proposed method, the comparisons are made with some popular and newly published skin detectors. The experimental results show that the proposed scheme outperforms other skin detection methods due to high precision and good recall. This method uses a specific way of training AdaBoost in skin detection while having a good accuracy and simplicity compared to other methods.