EURASIP Journal on Advances in Signal Processing
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
SIAM Journal on Computing
3D Fuzzy Vault Based on Palmprint
CYBERC '10 Proceedings of the 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery
Is Fuzzy Vault Scheme Very Effective for Key Binding in Biometric Cryptosystems?
CYBERC '11 Proceedings of the 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery
Fingerprint-Based Fuzzy Vault: Implementation and Performance
IEEE Transactions on Information Forensics and Security
A New Method for Generating an Invariant Iris Private Key Based on the Fuzzy Vault System
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Fuzzy vault scheme is one of the most popular biometric cryptosystems. However, the scheme is designed for set differences while Euclidean distance is often used in biometric techniques. Multidimensional fuzzy vault scheme (MDFVS) is a modified version that can be easily implemented based on biometric feature data. In MDFVS, every point is a vector, and Euclidean distance measure is used for genuine points filtering. To get better performances, the step of feature selection in the MDFVS algorithms is very important and should be well designed. In this paper we propose applying recognition rate to measure discrimination of features and selecting strong distinctive features into genuine points. Some principles of selecting strong distinctive features to compose genuine points are discussed. An implementation of MDFVS with feature selection is also presented. Experimental results based on palmprint show that the proposed feature selection approach improves the performances of MDFVS.