Adaptive multimodal biometric fusion algorithm using particle swarm

This paper introduces a new algorithm called “Adaptive Multimodal Biometric Fusion Algorithm”(AMBF), which is a combination of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received from multiple biometric sensors. The system’s accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the error cost and a priori probability of an intruder. This Bayesian framework formalizes the design of a system that can adaptively increase or reduce the security level. Particle swarm optimization searches the decision and sensor operating points (i.e. thresholds) space to achieve the desired security level. The optimization function aims to minimize the error in a Bayesian decision fusion. The particle swarm optimization algorithm results in the fusion rule and the operating points of sensors at which the system can work. This algorithm is important to systems designed with varying security needs and user access requirements. The adaptive algorithm is found to achieve desired security level and switch between different rules and sensor operating points for varying needs.

[1]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[2]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[3]  Anil K. Jain,et al.  Integrating Faces and Fingerprints for Personal Identification , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  John D Woodard BIOMETRICS: FACING UP TO TERRORISM. , 2001 .

[5]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[6]  L. Hong,et al.  Can multibiometrics improve performance , 1999 .

[7]  Pramod K. Varshney,et al.  Improving personal identification accuracy using multisensor fusion for building access control applications , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[8]  David Chandler,et al.  Biometric Product Testing Final Report , 2001 .

[9]  Russell C. Eberhart,et al.  Adaptive particle swarm optimization: detection and response to dynamic systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[10]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[11]  Thomas Kiel Rasmussen,et al.  Hybrid Particle Swarm Optimiser with breeding and subpopulations , 2001 .

[12]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[13]  Gerry Dozier,et al.  Adapting Particle Swarm Optimizationto Dynamic Environments , 2001 .

[14]  Anil K. Jain,et al.  Decision-Level Fusion in Fingerprint Verification , 2001, Multiple Classifier Systems.

[15]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[17]  Pramod K. Varshney,et al.  Distributed Detection and Data Fusion , 1996 .

[18]  Sharath Pankanti,et al.  Biometrics: Future of Identification , 2000 .

[19]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[20]  Anil K. Jain,et al.  Combining multiple matchers for a high security fingerprint verification system , 1999, Pattern Recognit. Lett..

[21]  Sharath Pankanti,et al.  Biometrics: The Future of Identification - Guest Editors' Introduction , 2000, Computer.

[22]  Robert Frischholz,et al.  BioID: A Multimodal Biometric Identification System , 2000, Computer.

[23]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.