Synthetic Data Augmentation for Facial Re-identification

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Abstract

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
Pages116-123
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
http://imvip.ie/#

Conference

ConferenceIrish Machine Vision and Image Processing Conference
Abbreviated titleIMVIP
CountryIreland
CityDublin
Period28/08/201930/08/2019
Internet address

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  • Cite this

    Brown, G., Martinez del Rincon, J., & Miller, P. (2019). Synthetic Data Augmentation for Facial Re-identification. In Proceeding of the Irish Machine Vision and Image Processing Conference 2019 (pp. 116-123). Irish Pattern Recognition & Classification Society. https://arrow.dit.ie/ditpress/11/