Customer-Driven Sensor Management
IEEE Intelligent Systems
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
A Self-tuning People Identification System from Split Face Components
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Multibiometric People Identification: A Self-tuning Architecture
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Concept-based evidential reasoning for multimodal fusion in human-computer interaction
Applied Soft Computing
Multibiometric cryptosystem: model structure and performance analysis
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
A new framework for adaptive multimodal biometrics management
IEEE Transactions on Information Forensics and Security
Individual identification using personality traits
Journal of Network and Computer Applications
Rank based hybrid multimodal fusion using PSO
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Expert Systems with Applications: An International Journal
Advances in the keystroke dynamics: the practical impact of database quality
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
| Hi-index | 0.00 |
This paper presents an evolutionary approach to the sensor management of a biometric security system that improves robustness. Multiple biometrics are fused at the decision level to support a system that can meet more challenging and varying accuracy requirements as well as address user needs such as ease of use and universality better than a single biometric system or static multimodal biometric system. The decision fusion rules are adapted to meet the varying system needs by particle swarm optimization, which is an evolutionary algorithm. This paper focuses on the details of this new sensor management algorithm and demonstrates its effectiveness. The evolutionary nature of adaptive, multimodal biometric management (AMBM) allows it to react in pseudoreal time to changing security needs as well as user needs. Error weights are modified to reflect the security and user needs of the system. The AMBM algorithm selects the fusion rule and sensor operating points to optimize system performance in terms of accuracy.