Fingerprint Intramodal Biometric System Based on ABC Feature Fusion

dc.contributor.authorJooda, Janet
dc.date.accessioned2022-05-04T08:06:48Z
dc.date.available2022-05-04T08:06:48Z
dc.date.issued2021-08-13
dc.description.abstractUnimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABCbased FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS.en_US
dc.identifier.issn2581-8260
dc.identifier.urihttp://dspace.run.edu.ng:8080/jspui/handle/123456789/2592
dc.language.isoenen_US
dc.publisherAsian Journal of Research in Computer Scienceen_US
dc.relation.ispartofseries11(2);1 - 10
dc.subjectIntramodal fusionen_US
dc.subjectfeature selectionen_US
dc.subjectartificial bee colonyen_US
dc.subjecttexture featuresen_US
dc.titleFingerprint Intramodal Biometric System Based on ABC Feature Fusionen_US
dc.typeArticleen_US
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