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• W4319068552 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." @default.
• W4319068552 created "2023-02-04" @default.
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• W4319068552 date "2023-01-01" @default.
• W4319068552 modified "2023-09-25" @default.
• W4319068552 title "GRETINA: A Large-Scale High-Quality Generated Retinal Image Dataset for Security and Privacy Assessment" @default.
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• W4319068552 doi "https://doi.org/10.1007/978-3-031-25825-1_27" @default.
• W4319068552 hasPublicationYear "2023" @default.
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