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GRETINA: A Large-Scale High-Quality Generated Retinal Image Dataset for Security and Privacy Assessment

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Image and Vision Computing (IVCNZ 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13836))

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Abstract

We present a generated dataset that is the largest and the first publicly shared high-quality synthetic retinal dataset. It is known that retinal patterns captured from humans are individual, even between identical twins. Despite the high accuracy and spoof resistance of retinal recognition systems, they have not reached the same level of maturity as the more popular face, fingerprint and iris. One cause is the lack of sufficient data for training and testing these systems. This paper reviews existing publicly available datasets of both real and generated retina images and identifies a lack of a large-scale high-quality retinal image dataset that can be used for security and privacy assessment. We fill this gap by using StyleGAN2-ADA to generate a synthetic dataset of five million high-quality retinal images from the limited available data.

The first author was supported by an RMIT University RD Gibson Grant and an RMIT University fee-waiver scholarship.

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Notes

  1. 1.

    Quality and fidelity are used interchangeably to show the resolution and clarity of images. Quality of the images is different from their diversity (distinctiveness).

  2. 2.

    https://github.com/mahshidsa/SG2-ADA-TheseRetinaeDoNotExist to support Reproducible Research (RR).

  3. 3.

    https://thispersondoesnotexist.com/.

  4. 4.

    https://github.com/mahshidsa/SG2-ADA-TheseRetinaeDoNotExist to support RR.

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Authors and Affiliations

  1. Mathematical Sciences, STEM College, RMIT University, Melbourne, Australia

    Mahshid Sadeghpour, Arathi Arakala, Stephen A. Davis & Kathy J. Horadam

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  1. Mahshid Sadeghpour
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  2. Arathi Arakala
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  3. Stephen A. Davis
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  4. Kathy J. Horadam
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Correspondence to Mahshid Sadeghpour .

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Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  2. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  3. Auckland University of Technology, Auckland, New Zealand

    Martin Stommel

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Sadeghpour, M., Arakala, A., Davis, S.A., Horadam, K.J. (2023). GRETINA: A Large-Scale High-Quality Generated Retinal Image Dataset for Security and Privacy Assessment. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_27

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  • DOI: https://doi.org/10.1007/978-3-031-25825-1_27

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