Handwritten Signature and Speech: Preliminary Experiments on Multiple Source and Classifiers for Personal Identity Verification

This paper presents a personal verification system based on two different biometric traits: handwritten signature and speech. The signature verification system uses contour-based features and a Dynamic Time Warping technique for matching. The speaker verification system uses cepstral based coefficients and is based on a Hidden Markov Model statistical classifier. In the decision combination stage, the decisions provided by the two systems are combined according to a simple abstract–level combination approach. The experimental results related to a real-scenario demonstrate the effectiveness of the proposed approach and highlight some profitable directions for further developments.