On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Secrets & Lies: Digital Security in a Networked World
Secrets & Lies: Digital Security in a Networked World
Handbook of Applied Cryptography
Handbook of Applied Cryptography
Enhancing security and privacy in biometrics-based authentication systems
IBM Systems Journal - End-to-end security
| Hi-index | 0.00 |
Biometrics-based authentication system offers advantages of providing high reliability and accuracy. However, the contemporary authentication system is impuissance to compromise. If a biometrics data is compromised, it cannot be replaced and rendered unusable. In this paper, a cancelable biometrics-based authenticator is proposed to solve this irrevocability issue. The proposed approach is a two-factor authentication system, which requires both of the random data and facial feature in order to access the system. In this system, tokenized pseudo-random data is coupled with moment-based facial feature via inner product algorithm. The output of the product is then discretized to generate a set of private binary code, coined as 2factor-Hashing code, which is acted as verification key. If this biometrics-based verification key is compromised, a new one can be issued by replacing a different set of random number via token replacement. Then, the compromised one is rendered completely useless. This feature offers an extra protection layer against biometrics fabrication since the verification code is replaceable. Experimental results demonstrate that the proposed system provides zero Equal Error Rate in which there is a clear separation in between the genuine and the imposter distribution populations.