A framework for improving accuracy of multimodal biometrics security based on bayesian network

This thesis addresses the problem of biometrics security and accuracy through the

fusion of fingerprint and face images. The Biometrics community in recent years

came up with different approaches to improve the accuracy and security of systems.

Multimodal authentication has attracted a lot of attention because of its advantage

over single biometrics matchers. Even though efforts were made to improve these

systems, they are, however, still vulnerable to security threats such as spoofing

where a forged biometric copy and/or artificially recreated biometric data (which

maybe legitimate) may be used to spoof the system. Multimodal biometric systems

overcome various limitations of uni-modal biometric systems, such as nonuniversality,

lower false acceptance, and higher genuine acceptance rates. We thus

propose a framework, based on Bayesian Network, which is tailored to deal with the

fusion of fingerprint and face. In our proposed framework, face and fingerprint data

are fused at Feature level, using probabilistic inference to map posterior probability

distributions of unobserved variable to make a basic decision of authentication. We

present our proposed framework for fusing bimodal biometrics systems by

combining face and fingerprint images to better the performance of biometrics

security system. In this research we wanted to exploit prior knowledge as much as

possible hence our use of the Bayesian Network to provide reasoning to our

framework. Although Bayesian Networks have been used before, our focus in this

study is to fully exploit the graphical structure of Bayesian Networks and explicitly

model their statistical dependencies between relevant variables per sample

measurement. The evaluation of the proposed framework used multimodal

chimerical databases formed from publicly available databases. The evaluation of

our framework shows an improved performance of 0.03 Error Equal Rate over the

performance of the face or finger biometric modalities when each is implemented.

The Error Equal Rate for Face and Finger biometric modalities were 0.31 and 0.28,

respectively.