A Novel Binarization Scheme for Real-Valued Biometric Feature

Abstract

Biometric binarization is the feature-type transformation that converts a specific feature representation into a binary representation. It is a fundamental issue to transform the real-valued feature vectors to the binary vectors in biometric template protection schemes. The transformed binary vectors should be high for both discriminability and privacy protection when they are employed as the input data for biometric cryptosystems. In this paper, we propose a novel binarization scheme based on random projection and random Support Vector Machine (SVM) to further enhance the security and privacy of biometric binary vectors. The proposed scheme can generate a binary vector of any given length as an ideal input for biometric cryptosystems. In addition, the proposed scheme is independent of the biometric feature data distribution. Several comparative experiments are conducted on multiple biometric databases to show the feasibility and efficiency of the proposed scheme.