Human skin detection, which is one of the important pre-processing phases, has a wide range of applications such as face tracking, skin diseases, video surveillance, web content filtering, and so on. Skin detection is a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination conditions, pose variations, race, aging, and complex background. Several methods dealing with skin detection assume that skin pixels can be extracted from background colors according to some thresholding rules related to a specific color model. Nevertheless, it is a complex task to recognize skin pixels under the challenging factors aforementioned. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of computer vision. However, we could find only a few researches that apply deep learning methods to deal with the skin detection problem. This paper presents a novel approach based on CNN for skin detection. Extensive experiments show that the proposed approach exceeds the best result for other state-of-the-art methods.

人体皮肤检测是重要的预处理阶段之一，具有广泛的应用，例如面部跟踪，皮肤疾病，视频监视，Web内容过滤等。皮肤检测是一个具有挑战性的问题，因为由于许多因素（例如光照条件，姿势变化，种族，老化和复杂的背景），皮肤的外观可能发生巨大变化。处理皮肤检测的几种方法假定可以根据与特定颜色模型有关的某些阈值规则从背景颜色中提取皮肤像素。然而，在上述挑战性因素下识别皮肤像素是一项复杂的任务。在最近的时代，深度卷积神经网络（CNN）的成功极大地影响了计算机视觉领域。然而，我们仅能找到一些应用深度学习方法来解决皮肤检测问题的研究。本文提出了一种基于CNN的皮肤检测新方法。大量实验表明，所提出的方法超出了其他最新方法的最佳结果。