Synthetic Data Augmentation for Facial Re-identification
Synthetic Data Augmentation for Facial Re-identification
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发表时间：
2019-07
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通讯作者：
G. Brown;J. M. D. Rincón;P. Miller
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作者：
G. Brown;J. M. D. Rincón;P. Miller
有助于训练深度神经网络（DNN）的面部重新识别数据集往往是从互联网上收集的名人的高质量图像。然而，这些数据集与现实世界系统和监控录像等场景中使用的低质量样本之间存在领域差距。在这项工作中，我们描述了一种新的数据增强过程中，使用合成生成的图像，这有助于跨域概括性，而不需要在目标域中获取大量的真实的数据。我们还贡献了一个来自这个过程的新数据集：syn-Face。我们的方法通过使用具有合成增强的标准高质量数据集进行训练并在2个不同的现实集中进行测试来验证。
Facial Re-identification datasets which facilitate the training of Deep Neural Networks (DNNs), tend to be high quality images of celebrities harvested from the internet. There is however a domain gap between these datasets, and the low quality samples used in real-world systems and scenarios such as surveillance footage. In this work we describe a novel process of data augmentation using synthetically generated images, which aids cross-domain generalisability, without the need to acquire large amounts of real data in the target domain. We also contribute a new dataset derived from this process: syn-Face . Our approach is validated by training with standard high quality datasets with synthetic augmentation and testing in 2 different realistic sets.