Integration of multiple cues for a human authentication system

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In the field of biometrics, personal authentication and identification are regarded as effective methods for automatic recognition of a person. Using multimodal biometric systems, we typically get better recognition performance compared to using a single biometric modality. We propose a multimodal biometric system using two modalities: palmprint and speech. Integrating the palmprint and speech features increases the recognition performance. We extract the discriminant features using a modified canonical form method for the Palmprint and the Mel Frequency Cepstral Coefficients (MFCC) technique for speech. The final decision is then made by fusion at the matching score level. Using a large database as the test set, the experimental results show significant improvement in reliability of recognition systems and demonstrate increases in the recognition rate.