Biohashing with Local Zernike Moments for Face Verification

Local Zernike moments (LZM) is a recently proposed image representation scheme that is shown to be successful for face representation. In this study, a face verification system which depends on biometric hashing scheme, that uses LZM features extracted from face images is proposed. With the proposed system, security and user privacy is ensured. Verification performance of the system, in which user specific secret keys are used for biometric hashing, is evaluated in two different scenarios, on the BioSecure face database. In the first scenario, biometric hashing is realized using each user's secret key and %0 equal error is obtained. In the second scenario, in which the secret key of a user is stolen by an adversary, biometric hashes are created using the stolen key and any biometric sample. In this case, the equal error rate increases to %8, 26, which is comparable to equal error rate of %6, 81, where only LZM feature vectors are used for verification.