Adaptive Pso Integration Of Multi-modal Biometric Identification Methods

Biometrics is the technology which refers to identifying an individual based on hisor her physiological or behavioral characteristics. Compared to traditional identificationand verification methods, biometrics is more convenient for users, reduces fraud, andcan not be forgotten or replaced. Biometrics has been proven to be successful and hasbeen applied in some fields, however, each single biometric modality (unimodal) has itsadvantages as well as drawbacks, and the error rates associated with unimodal biometricsystem are quite high which makes them unacceptable for deployment in securitycritical applications. Some of the problems that affect unimodal biometric system can bealleviated by using multimodal biometric traits. Systems that fuse multiple cuesobtained from two or more biometric indicators for the purpose of person recognitionare called multimodal biometric systems. Multimodal strategy and fusion scheme cansignificantly improve the overall accuracy of the biometric system. Multimodalbiometrics has been receiving a lot of attention in the recent years.This dissertation presents an Adaptive Particle Swarm Optimization Fusion(APSOF) algorithm which can fuse multiple biometric modalities at the decision level.The fusion problem is designed as a Bayesian decision framework and the APSOFalgorithm can automatically adjust the optimum decision fusion rules to minimize theBayesian error cost for the fusion system. To improve the performance of the fusionalgorithm, this thesis also proposes a novel Minimum Velocity Limited PSO. Theminimum velocity strategy applies a threshold to control the flying velocities of PSOparticles, thus improves the convergence ability and stability of the algorithm.To demonstrate the performance of APSOF algorithm, this dissertation uses thisfusion scheme to fuse two biometric modalities face and fingerprint. The experimentsare designed and carried out on the ORL, UMIST face database and MCYT fingerprintdatabase. Experimental results show that multimodal fusion scheme outperformsunimodal system based on face or fingerprint. It is proven that APSOF algorithm canselect the optimum decision fusion rules adaptively according to the variation of theaccuracy of unimodal system. Based on APSOF algorithm, I developed a Multimodal Biometric Identification System (MultiBIS). This thesis describes the design,implementation and functions of MultiBIS.