A multi-modal method based on the competitors of FVC2004 and on palm data combined with tokenised random numbers

In this work, we propose a multi-modal method that combines the scores of selected fingerprint matchers with the score obtained by a palm authenticator where the palm features are combined with pseudo-random numbers. We use a random subspace of AdaBoost.M1 to combine the scores of the best fingerprint matchers automatically selected among the competitors of FVC2004, and we study the performance when a different number of competitors are involved in the fusion. Moreover a deep study is carried out to design a hybrid system based on the combination of palm image features and a personal key.In conclusion, the aim of this work is to design a multi-modal biometric system and to analyze the benefits and the limits of fusion approaches in order to boost the performance of hybrid system in the worst testing hypothesis when an “impostor” steal the personal key of the user A before he tries to authenticate as A. The experimental results reported in this paper confirm that using a multi-modal ensemble of matchers it is possible to overcome some of the limitations of each single matcher leading to a considerable performance improvement.