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
翻译:本文探讨了在不依赖颜色信息的情况下在图像中自动检测人类皮肤的问题。 这项工作的主要动机是,在使用明显偏向于较轻皮肤的训练数据集的同时,取得与整个皮肤色调一致的成果。 以前的皮肤检测方法几乎完全使用颜色提示,我们提出了一个在没有这种信息的情况下运行良好的新方法。 工作的一个重要方面是,通过在培训期间战略性地应用的增强功能来修复数据集,目标是通过彩色变异特征学习来强化普遍性。 我们已经用两个结构展示了这一概念,实验结果显示,在基准ECU数据集中大多数菲茨帕特里克皮肤色调的精确度和回溯方面都有改进。 我们进一步用RFW数据集测试了系统,以显示拟议方法在不同种族间更加一致地运行,从而减少了基于肤色的偏差的可能性。 为了证明我们的工作的有效性,对灰度图像以及未经控制的照明和人工过滤下获得的图像进行了广泛的实验。 源代码: https://githubab.com/HanXimmentation/Colant。