Fusing Speech Signal and Palmprint Features for an Secured Authentication System

In the application of Biometric authentication, personal identification is regarded as an effective method for automatic recognition, with a high confidence, a person's identity. Using multimodal biometric systems we typically get better performance compare to single biometric modality. This paper proposes the multimodal biometrics system for identity verification using two traits, i.e., speech signal and palmprint. Integrating the palmprint and speech information increases robustness of person authentication. The proposed system is designed for applications where the training data contains a speech signal and palmprint. It is well known that the performance of person authentication using only speech signal or palmprint is deteriorated by feature changes with time. The final decision is made by fusion at matching score level architecture in which feature vectors are created independently for query measures and are then compared to the enrolment templates, which are stored during database preparation.