Methodology for Building Synthetic Datasets with Virtual Humans
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Shubhajit Basak, Hossein Javidnia, Faisal Khan, Rachel McDonnell, Michael Schukat
2020
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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