论文标题
颜色不变的皮肤细分
Color Invariant Skin Segmentation
论文作者
论文摘要
本文解决了在不依赖颜色信息的情况下自动检测到人类皮肤的问题。这项工作的主要动机是取得在整个肤色范围内保持一致的结果，即使使用训练数据集则显着偏向较轻的肤色。以前的皮肤检测方法几乎完全使用了颜色提示，我们提出了一种在没有此类信息的情况下表现良好的新方法。该工作的一个关键方面是通过培训期间战略应用的增强来维修数据集维修，其目标是不变特征学习以增强概括。我们已经使用两个体系结构证明了这一概念，实验结果表明，基准ECU数据集中大多数Fitzpatrick肤色的精度和回忆都有所改善。我们使用RFW数据集进一步测试了该系统，以表明所提出的方法在不同种族之间的性能更加一致，从而减少了基于肤色的偏见的机会。为了证明我们工作的有效性，对灰度图像以及在无约束的照明和人工过滤器下获得的图像进行了广泛的实验。源代码：https：//github.com/hanxumartin/color-invariant-skin-细分
This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, with the goal of color invariant feature learning to enhance generalization. We have demonstrated the concept using two architectures, and experimental results show improvements in both precision and recall for most Fitzpatrick skin tones in the benchmark ECU dataset. We further tested the system with the RFW dataset to show that the proposed method performs much more consistently across different ethnicities, thereby reducing the chance of bias based on skin color. To demonstrate the effectiveness of our work, extensive experiments were performed on grayscale images as well as images obtained under unconstrained illumination and with artificial filters. Source code: https://github.com/HanXuMartin/Color-Invariant-Skin-Segmentation
