TY - GEN
T1 - MLBPR
T2 - MAS for large-scale biometric pattern recognition
AU - Meshulam, Ram
AU - Reches, Shulamit
AU - Yarden, Aner
AU - Kraus, Sarit
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70450189531&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04879-1_19
DO - 10.1007/978-3-642-04879-1_19
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AN - SCOPUS:70450189531
SN - 3642048781
SN - 9783642048784
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 274
EP - 292
BT - Safety and Security in Multiagent Systems - Research Results from 2004-2006
A2 - Barley, Mike
A2 - Mouratidis, Haralambos
A2 - Unruh, Amy
A2 - Spears, Diana
A2 - Scerri, Paul
A2 - Massacci, Fabio
ER -