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Multimodal Personal Authentication with Fingerprint, Speech and Teeth Traits Using SVM Classifier

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Executive Summary

Multimodal biometrics systems are becoming increasingly efficient over the unimodal system, especially for the securing mobile devices like PDA, PC tablets and, etc. In this paper, the authors propose a novel tri-modal biometric recognition technique using teeth, fingerprint and voice as biometric traits. The matching scores of the individual traits are classified using support vector machine. The experiments were conducted over a database collected from 20 individuals with multiple instances of all the three traits. The performance analysis of the fusion techniques revealed that the equal error rates of 1.44%, 1.88% and 3.06% for the support vector machine, weighted summation and K-NN Classifier respectively.

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