Binary Biometrics: An Analytic Framework to Estimate the Performance Curves Under Gaussian Assumption

In recent years the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helper data system, fuzzy extractors, fuzzy vault and cancellable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives or error-correcting codes (ECC) and use a binary representation of the real-valued biometric data. Hence, the difference between two biometric samples is given by the Hamming distance or bit errors between the binary vectors obtained from the enrollment and verification phases respectively. If the Hamming distance is smaller (larger) than the decision threshold, then the subject is accepted (rejected) as genuine. Because of the use of ECCs, this decision threshold is limited to the maximum error-correcting capacity of the code, consequently limiting the false rejection rate (FRR) and false acceptance rate (FAR) trade-off. A method to improve the FRR consists ...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Emile J. C. Kelkboom, Gary Garcia Molina, Jeroen Breebaart, Raymond N. J. Veldhuis, Tom A. M. Kevenaar, Willem Jonker
• Keywords: Artificial Intelligence, Biometric, Biometric Data, Hamming Distance, TSMC 2010