Mutual dependency of features in multimodal biometric systems

Abstract

Multimodal biometric systems have many advantages over single-modal biometric systems, e.g. higher recognition accuracy and security level. However, for most existing multimodal biometric systems, experiments are conducted using the unreal multimodal databases in which no samples of different biometric modules are from the same person. For example, a fingerprint sample of person A and a biometric face sample of person B will be combined to form a sample of the joint fingerprint module and face module. A multimodal biometric system is designed and tested, which is made up of two common biometrics, face and fingerprint, using a real multimodal database (a sample of the joint fingerprint module and face module is formed from the fingerprint and face of the same person) and two unreal multimodal databases. From the experimental results it is observed that there is a large discrepancy between the system performances evaluated with the real and unreal multimodal databases. This indicates that ignoring the influence of feature dependency, which has been a common practice in evaluating multimodal biometrics systems, can produce misleading system performance evaluation results.