Bimodal Biometric System for Cryptographic Key Generation Using Wavelet Transforms

In this paper we present a bimodal biometric system for cryptographic key generation that works with speech and electrocardiogram (ECG) signals using wavelet transforms. This work is based on the uniqueness and quasi-stationary behavior of ECG and speech signals with respect to an individual. The architecture of the proposed system considers three security factors, namely, user password, biometric samples, and a token. The stages that comprise the architecture are \textit{one time enrollment} and \textit{key derivation}. The system architecture is able to verify the identity of individuals off-line avoiding the use of a centralized database for storing the biometric information. The system also implements an error-correction layer using the Hadamard code. The performance of the system is assessed using ECG signals from the MIT-BIH database and speech signals from a speech database created for testing purposes. Simulation results report a false acceptance rate (FAR) of 1.27% and a false rejection rate (FRR) of 10.62% for the system. The random cryptographic key released by the system may be used in several encryption algorithms.