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.
