MLBPR: MAS for large-scale biometric pattern recognition

Security systems can observe and hear almost anyone everywhere. However, it is impossible to employ an adequate number of human experts to analyze the information explosion. In this paper, we present a multi-agent framework which works in large-scale scenarios and responds in real time. The input for the framework is biometric information acquired at a set of locations. The framework aims to point out individuals who act according to a suspicious pattern across these locations. The framework works in large-scale scenarios. We present two scenarios to demonstrate the usefulness of the framework. The goal in the first scenario is to point out individuals who visited a sequence of airports, using face recognition algorithms. The goal in the second scenario is to point out individuals who called a set of phones, using speaker recognition algorithms. Theoretical performance analysis and simulation results show a high overall accuracy of our system in real-time.