Grupo de Aplicación de Telecomunicaciones Visuales, Universidad Politecnica de Madrid, 28040 Madrid, Spain.
Computer Vision Lab, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
Sensors (Basel). 2019 Mar 4;19(5):1103. doi: 10.3390/s19051103.
Latest advances of deep learning paradigm and 3D imaging systems have raised the necessity for more complete datasets that allow exploitation of facial features such as pose, gender or age. In our work, we propose a new facial dataset collected with an innovative RGB⁻D multi-camera setup whose optimization is presented and validated. 3DWF includes 3D raw and registered data collection for 92 persons from low-cost RGB⁻D sensing devices to commercial scanners with great accuracy. 3DWF provides a complete dataset with relevant and accurate visual information for different tasks related to facial properties such as face tracking or 3D face reconstruction by means of annotated density normalized 2K clouds and RGB⁻D streams. In addition, we validate the reliability of our proposal by an original data augmentation method from a massive set of face meshes for facial landmark detection in 2D domain, and by head pose classification through common Machine Learning techniques directed towards proving alignment of collected data.
深度学习范式和 3D 成像系统的最新进展提出了对更完整数据集的需求，这些数据集允许利用面部特征，如姿势、性别或年龄。在我们的工作中，我们提出了一个新的面部数据集，该数据集是使用创新的 RGB⁻D 多摄像机设置收集的，我们对其进行了优化并进行了验证。3DWF 包括 92 个人的 3D 原始和注册数据采集，这些数据来自低成本的 RGB⁻D 感应设备和具有高精度的商业扫描仪。3DWF 提供了一个完整的数据集，其中包含与面部属性相关的相关和准确的视觉信息，例如通过注释密度归一化的 2K 云以及 RGB⁻D 流进行面部跟踪或 3D 面部重建。此外，我们通过一种原始的数据增强方法从大量的面部网格中验证了我们的提议的可靠性，用于 2D 域中的面部地标检测，并且通过常见的机器学习技术进行头部姿势分类，旨在证明所收集数据的对齐。
