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Random permutation-based linear regression for cancelable biometrics
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DOI：10.1111/exsy.13652.png)
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The security of biometric data in biometric-based authentication systems is a significant concern. Cancellable biometrics aim to generate templates that can be replaced by new templates if compromised. We propose a new approach for generating cancellable biometric templates based on linear regression with random permutation. Our approach generates a virtual image for every biometric image by applying linear regression. In the next step, the cancellable biometric template is produced by randomly permuting each virtual image depending on a key assigned to each individual. If the template is compromised, it can be cancelled, and a new template can be generated by altering the key. Our method has shown superior performance compared to existing random permutation-based methods in terms of authentication accuracy across six databases, encompassing face, iris, and ear, even when dealing with low-resolution images. It performed well on challenging databases like UBIRIS and Georgia Tech, demonstrating the robustness of the proposed approach.
Keyword:
cancellable biometric
linear regression
random permutation
template generation
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2.3
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被引数:
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APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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