Signal and image processing with neural networks: a C++ sourcebook
Signal and image processing with neural networks: a C++ sourcebook
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Unobtrusive multimodal biometric authentication: the HUMABIO project concept
EURASIP Journal on Advances in Signal Processing
Classifier combining rules under independence assumptions
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A score-level fusion benchmark database for biometric authentication
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fuzzy Fusion of Eyelid Activity Indicators for Hypovigilance-Related Accident Prediction
IEEE Transactions on Intelligent Transportation Systems
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We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural Networks (ANNs), Fuzzy Expert Systems (FESs), and Support VectorMachines (SVMs). The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns froma well-known benchmark biometrics database, and the validation/testing took place with patterns fromthe same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature.