Synthetic Data Augmentation for Facial Re-identification

Glen Brown, Jesus Martinez del Rincon, Paul Miller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceeding of the Irish Machine Vision and Image Processing Conference 2019
PublisherIrish Pattern Recognition & Classification Society
ISBN (Electronic)978-0-9934207-4-0
Publication statusEarly online date - 28 Aug 2019
EventIrish Machine Vision and Image Processing Conference - Grangegorman Campus, Dublin, Ireland
Duration: 28 Aug 201930 Aug 2019
Conference number: 2019


ConferenceIrish Machine Vision and Image Processing Conference
Abbreviated titleIMVIP
Internet address


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