Human facial skin images contain abundant textural information that can serve as valuable features for attribute classification, such as age, race, and gender. Additionally, facial skin images offer the advantages of easy collection and minimal privacy concerns. However, the availability of well-labeled human skin datasets with a sufficient number of images is limited. To address this issue, we introduce a dataset called FaceSkin, which encompasses a diverse range of ages and races. Furthermore, to broaden the application scenarios, we incorporate synthetic skin-patches obtained from 2D and 3D attack images, including printed paper, replays, and 3D masks. We evaluate the FaceSkin dataset across distinct categories and present experimental results demonstrating its effectiveness in attribute classification, as well as its potential for various downstream tasks, such as Face anti-spoofing and Age estimation.

翻译：摘要：人类面部皮肤图像包含丰富的纹理信息，这些信息可作为年龄、种族和性别等属性分类的重要特征。此外，面部皮肤图像具有易于采集且隐私问题较少的优势。然而，目前带有充分标注且图像数量充足的人类皮肤数据集仍然有限。为解决这一问题，我们引入了一个名为FaceSkin的数据集，该数据集涵盖不同年龄和种族。同时，为拓展应用场景，我们整合了从2D和3D攻击图像（包括打印纸、重放攻击和3D面具）中获取的合成皮肤块。我们针对不同类别对FaceSkin数据集进行了评估，并展示了实验结果表明该数据集在属性分类中的有效性，以及在人脸活体检测和年龄估计等多种下游任务中的潜力。