In this paper, we present a new biometric verification system. The proposed system employs a novel biometric hashing scheme that uses our proposed quantization method. The proposed quantization method is based on error-correcting output codes which are used for classification problems in the literature. We improve the performance of the random projection based biometric hashing scheme proposed by Ngo et al. in the literature [5]. We evaluate the performance of the novel biometric hashing scheme with two use case scenarios including the case where an attacker steals the secret key of a legitimate user. Simulation results demonstrate the superior performance of the proposed scheme.

Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussian mixture model at the quantization step. Our simulation results on three different databases demonstrate that the proposed method has superior performance in comparison to previously proposed random projection based methods.