Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple matchers for a high security fingerprint verification system
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Swarm intelligence
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
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In this paper an adaptive sensor management algorithm is presented for a biometric sensor network. A distributed detection framework is adapted for varying security requirements in the network, by considering the trade-offs between accuracy and time. Accuracy and time are tied into a single weighted objective function and a particle swarm optimisation algorithm is designed to achieve best possible configurations for a given set of weights. Results are presented for different weights applied to the bi-objective problem. A Bayesian framework is proposed for estimating the a priori of the imposter in real time. This determines the security requirements of the network. The estimation uses the observations collected from the sensors for different individuals accessing the network via the distributed detection framework. The distributed detection framework is redesigned for the new updated a priori, resulting in a closed loop control of a biometric sensor network. Results show that the new adaptive sensor management algorithm leads to lower false acceptance and false rejection rates when compared to a network without the adaptive algorithm.