University of Bahrain
Scientific Journals

Multimodal Biometric Watermarking-based Transfer Learning Authentication

Show simple item record Ghouzali, Sanaa 2022-12-06T20:16:03Z 2022-12-06T20:16:03Z 2022-12-06
dc.identifier.issn 2210-142X
dc.description.abstract Though biometric-based authentication systems have inherent advantages over conventional authentication systems, which use passwords and ID cards, these systems cannot ensure the security and privacy of biometric data stored in their databases. Recently, several watermarking-based algorithms have been efficiently used to protect biometric templates. However, these methods also require storing the watermarked biometrics for the matching purpose of the query during the authentication phase. The paper aims to develop an approach for authenticating watermarked biometrics using transfer learning without the need to store biometric data. Benchmark face and fingerprint databases are employed to conduct the experimentation. The obtained results validated the proposed approach’s ability to discriminate between different users with a performance accuracy rate achieving 99.17% while protecting the user’s biometrics. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Watermarking, Biometrics, Multimodal, Deep learning, Transfer learning en_US
dc.title Multimodal Biometric Watermarking-based Transfer Learning Authentication en_US
dc.type Article en_US
dc.volume 12 en_US
dc.issue 1 en_US
dc.pagestart 1375 en_US
dc.pageend 1382 en_US
dc.contributor.authoraffiliation Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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