Abstract
Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to tracking body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent research in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work is a framework to evaluate and combine different skin detector approaches, whose source code will be made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the ensemble here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and ensemble proposed in this paper will be freely available from (https://www.dei.unipd.it/node/2357 + Pattern Recognition and Ensemble Classifiers).
Abstract (translated)
皮肤检测是在数字图像中识别皮肤和非皮肤区域的过程，广泛应用于手势分析、身体部位跟踪和面部检测等多种应用中。皮肤检测是一个具有挑战性的问题，已经引起了研究界的广泛关注，但是由于缺乏一个共同的基准和统一的测试协议，在方法之间进行公平比较是非常困难的。在这项工作中，我们调查了这一领域的最新研究，并提出了使用几种不同数据集的方法之间的公平比较。这项工作的主要贡献是评估和结合不同的皮肤探测器方法的框架，这些方法的源代码将免费提供给未来的研究，以及在几种最近的方法之间的广泛的实验比较，这些方法也被用来定义一个集合。这在许多不同的问题上都很有效。实验在10个不同的数据集中进行，包括10000多个标记图像：实验结果证实，本文提出的集成相对于其他独立方法获得了非常好的性能，无需特别的参数调整。本文提出的测试和集成框架的matlab版本可从（https://www.dei.unipd.it/node/2357+模式识别和集成分类器）免费获得。
URL
https://arxiv.org/abs/1802.02531
