Template Calibration Parameter Optimization of Fuzzy Vault Method

Abstract

In the concrete implementation of the fuzzy vault algorithm, the geometric hash method is a common technique for automatic calibration of biometric templates. For the fuzzy problem of parameter acquisition, the matching accuracy of fuzzy vault template is affected in the three parameters: the pixel size, hash table and hash table quantization parameters ([Formula: see text] and [Formula: see text]). The single factor experiment method obtains the optimal range of these three parameters, and the extraction range of the fuzzy point and the selection rule of the base point distance are improved for the fuzzy vault algorithm. Finally, based on the FVC fingerprint database, their matching precision is compared for the algorithm before and after optimization. The experimental results show that the false rejection rate (FRR) of the optimized algorithm is reduced by at least 9.84%, and the false acceptance rate (FAR) is reduced by at least 7.12%, indicating that the optimization scheme improves the matching accuracy of the algorithm. The algorithm has certain robustness and practicability.