PhD Dissertation Defense-Muhammet Yıldız
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Biometric layering: template security and privacy through multi-biometric template fusion


Muhammet YILDIZ
CS, PhD Dissertation, 2016


Thesis Jury

Prof. Dr. Berrin YANIKOĞLU (Thesis Advisor),

Prof. Dr. Albert LEVİ

Assoc. Prof. Dr. Hakan ERDOĞAN

Assoc. Prof. Dr. Mehmet GÖKTÜRK

Asst. Prof. Dr. Yakup GENÇ




Date & Time: May 31th, 2016 –  13:00 AM

Place: FENS-G032

Keywords : biometrics, multibiometrics, fingerprint, voice, minutiae, layering




As biometric applications are gaining popularity, there is increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. Biometric template protection mechanisms suggested in recent years aim to address these issues by securing the biometric data in a template or other structure such that it is suitable for authentication purposes, while being protected against unauthorized access or cross- linking attacks.

We propose a biometric authentication framework for enhancing privacy and template security, by layering multiple biometric modalities to construct a multi-biometric tem- plate such that it is difficult to extract or separate the individual layers. Thus, the framework uses the subject’s own biometric to conceal her biometric data, while it also enjoys the performance benefits because of the use of multiple modalities. The resulting biometric template is also cancelable if the system is implemented with cancelable bio- metrics such as voice. We present two different realizations of this idea: one combining two different fingerprints and another one combining a fingerprint and a spoken pass- phrase. In either case, both biometric samples are required for successful authentication, leading to increased security, in addition to privacy gains.

The performance of the proposed framework is evaluated using the FVC 2000-2002 and NIST fingerprint databases, and the TUBITAK MTRD speaker database. Results show only a small degradation in EER compared to a state-of-the-art fingerprint verification system and high identification rates, while cross-link rates are low even with very small databases.