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Four Machine Learning Algorithms for Biometrics Fusion : A Comparative Study
Joint Authors
Argyropoulos, S.
Damousis, I. G.
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-03-18
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
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 Vector Machines (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 from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the 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.
American Psychological Association (APA)
Damousis, I. G.& Argyropoulos, S.. 2012. Four Machine Learning Algorithms for Biometrics Fusion : A Comparative Study. Applied Computational Intelligence and Soft Computing،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-456705
Modern Language Association (MLA)
Damousis, I. G.& Argyropoulos, S.. Four Machine Learning Algorithms for Biometrics Fusion : A Comparative Study. Applied Computational Intelligence and Soft Computing No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-456705
American Medical Association (AMA)
Damousis, I. G.& Argyropoulos, S.. Four Machine Learning Algorithms for Biometrics Fusion : A Comparative Study. Applied Computational Intelligence and Soft Computing. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-456705
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-456705