Synthetic data augmentation for facial re-identification
| Autor: | Glen Brown, Jesus Martinez-del-Rincon, Paul Miller |
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| Přispěvatelé: | Courtney, Jane, Deegan, Catherine, Leamy, Paul |
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| Zdroj: | Queen's University Belfast-PURE Brown, G, Martinez del Rincon, J & Miller, P 2019, Synthetic data augmentation for facial re-identification . in J Courtney, C Deegan & P Leamy (eds), Proceeding of the 21st Irish Machine Vision and Image Processing Conference, IMVIP 2019 . Irish Pattern Recognition & Classification Society, pp. 116-123, 21st Irish Machine Vision and Image Processing Conference, Dublin, Ireland, 28/08/2019 . |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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