Performance Evaluation Of Unsupervised Learning Algorithm In Biometric Based Fraud Prevention System
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 10
Abstract
Recently biometrics is the best alternative for the token based and knowledge based security systems. Unlike commonly used traditional identification technology based on passwords and keys, biometrics is more reliable, more convenient and more secure. Several algorithms have been employed, especially supervised learning algorithms, as data classifications. This paper implement unsupervised learning algorithm in multi-modal biometric system for its suitability. The system architecture consists of morphological pre-processing, feature selections, feature level fusion by concatenation, and matching stages. The performance of the Self Organizing Feature Map is compared with back-propagation neural network. The processed data were matched for recognition using self organizing feature map and back-propagation neural network algorithms for performance. The back-propagation neural network produced recognition accuracy rate of 93.7, genuine acceptance rate of 98.4, and false acceptance rate of 7.7 while self organizing feature map yielded recognition accuracy rate of 93.5, genuine acceptance rate of 93.7, and false acceptance rate of 7.8. And it was deduced from the results that self organizing feature map relatively well as back-propagation neural network.
Authors and Affiliations
Ismaila W. Oladimeji, Shittu Jaleel K. , Ismaila Folasade M, Ajayi Ayomide O.
Keywords
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- EP ID EP401969
- DOI 10.9790/9622-0810016267.
- Views 154
- Downloads 0
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