Open AccessFingerprint Intramodal Biometric System Based on ABC Feature Fusion
Author(s) -
Janet O. Jooda,
Alice O. Oke,
E. O. Omidiora,
Olufunso Adebola Adedeji,
Bukola Oyeladun Makinde
Publication year - 2021
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2021/v11i230256
Subject(s) - feature selection , pattern recognition (psychology) , artificial intelligence , biometrics , computer science , curse of dimensionality , feature vector , fingerprint (computing) , feature (linguistics) , feature extraction , fusion , philosophy , linguistics
Unimodal 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 ABC-based 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.