A family of methods for quality-based multimodal biometric fusion using generative classifiers

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. This paper considers how auxiliary information such as the quality associated with a biometric sample and the device information can be used when combining the output of several biometric devices. Since both these sources of information are not discriminative in distinguishing genuine users from impostors, combining them is indeed a challenging problem. We advance the state of the art of multimodal biometric fusion in two ways: first, we unify several existing generative classifiers using Bayesian networks. Second, we propose a novel fusion classifier incorporating both the quality and device information simultaneously. Our experiments based on the Biosecure DS2 dataset suggests that the proposed classifier can systematically achieve the best generalization performance compared to currently available state-of-the-art classifiers.