Methodology for Building Synthetic Datasets with Virtual Humans

Shubhajit Basak, Hossein Javidnia, Faisal Khan, R. Mcdonnell, M. Schukat
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引用次数: 5

Abstract

Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset. Repeatability of data is another challenge as it is not possible to exactly recreate ‘real-world’ acquisition conditions outside of the laboratory. In this work, we explore a framework to synthetically generate facial data to be used as part of a toolchain to generate very large facial datasets with a high degree of control over facial and environmental variations. Such large datasets can be used for improved, targeted training of deep neural networks. In particular, we make use of a 3D morphable face model for the rendering of multiple 2D images across a dataset of 100 synthetic identities, providing full control over image variations such as pose, illumination, and background.
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用虚拟人构建合成数据集的方法
深度学习方法的最新进展提高了人脸检测和识别系统的性能。这些模型的准确性依赖于训练数据中提供的变化范围。创建一个代表所有真实世界面孔变化的数据集是不可行的,因为对数据质量的控制随着数据集的大小而降低。数据的可重复性是另一个挑战,因为不可能在实验室之外准确地重现“真实世界”的采集条件。在这项工作中,我们探索了一个框架来综合生成面部数据,作为工具链的一部分,用于生成对面部和环境变化具有高度控制的非常大的面部数据集。这样的大型数据集可以用于改进深度神经网络的针对性训练。特别是,我们利用3D变形面部模型在100个合成身份的数据集上渲染多个2D图像,提供对图像变化的完全控制,如姿势、照明和背景。
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