Melanins and sebums are two important criteria for the quality evaluation of skin, and they are capable of providing customized suggestions for skin care. Currently, their detection is heavily relied on manual process performed by specialists in laboratory. Although efficient, such a manual detection is an expensive and labor-intensive procedure, and hence, there has been great interest in developing computational models for automatic detection of melanins and sebums from skin images. In this work, we propose an automatic detection algorithm, namely DAME, to identify these two kinds of substances based on a generative adversarial network (GAN). To do so, DAME makes use of a variant of GAN, i.e., pix2pix, due to its strength in image generation by learning the structural and contextual information of melanins and sebums observed from skin images. With these additional augmented images, a robust U-Net model can be learned for automatically detecting and marking melanins and sebums. To evaluate the performance of DAME, we have conducted a series of experiments by comparing it with several existing algorithms on real image datasets, and the results have demonstrated that DAME yields a substantially better detection accuracy than previously published algorithms in terms of several independent evaluation metrics. Moreover, DAME is believed to be more robust than other algorithms, as it obtains the smallest variance for each metric. Hence, DAME makes it possible to automatically detect melanins and sebums with a promising performance. Due to the strong learning ability of GAN, DAME is also able to identify melanins and sebums that are possibly ignored by specialists. The source codes of DAME and datasets used in the experiments are available at https://github.com/reBioco-der/DAME.

黑色素和皮脂是评估皮肤质量的两个重要标准，它们能够为皮肤护理提供个性化的建议。当前，它们的检测严重依赖于实验室专家进行的手动处理。尽管有效，但是这种手动检测是昂贵且费力的过程，因此，人们非常关注开发用于从皮肤图像自动检测黑色素和皮脂的计算模型。在这项工作中，我们提出了一种自动检测算法，即DAME，以基于生成对抗网络（GAN）来识别这两种物质。为此，由于DAME通过学习从皮肤图像中观察到的黑色素和皮脂的结构和背景信息，在图像生成方面具有优势，因此利用了GAN的一种变体，即pix2pix。利用这些附加的增强图像，可以学习一个强大的U-Net模型，以自动检测和标记黑色素和皮脂。为了评估DAME的性能，我们通过将其与真实图像数据集上的几种现有算法进行比较，进行了一系列实验，结果表明，就几种独立的评估指标而言，DAME的检测准确度明显高于先前发布的算法。 。此外，DAME被认为比其他算法更健壮，因为它为每个度量获取最小的方差。因此，DAME可以自动检测具有良好性能的黑色素和皮脂。由于GAN具有强大的学习能力，DAME还能识别可能被专家忽略的黑色素和皮脂。