A multi-agent based enhancement for multimodal biometric system at border control

Abstract

Border criminals and unauthorized immigrants are dramatically increasing within the last few years in globally due to the absence of proper authorization methods at the border locations. Usually, passenger controls done by trained immigration officers who compare the passport and the physical appearance at the border while some of the countries done by automated border control (ABC) systems. ABC is one of the applicable real-world applications of the biometric domain which commonly implements with fingerprint and face (multimodal) biometric authorization. However, selecting an appropriate classification method is a challenging task at the decision-making stage. This paper proposes a novel architecture for multimodal biometric authorization engaged with the multi-agent system (MAS) to come up with the optimal solution by using the co-feature of MAS, such as coordination, corporation, and negotiation features. The experiment was done with four available multimodal datasets, namely, the National Institute of Standard Technology (NIST) multimodal, SDUMLA-HMT multimodal, BANCA and PRIVATE databases have been reported to demonstrate the efficiency of the proposed method. The experimental result delivers an excellent performance comparing with previous ABC systems at the authentication phase and computationally fast.

Keywords

• Artificial intelligence
• Biometrics
• Multi-agent system
• Border control system