Fingerprint Intramodal Biometric System Based on ABC Feature Fusion
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Date
2021-08-13
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Publisher
Asian Journal of Research in Computer Science
Abstract
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 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.
Description
Keywords
Intramodal fusion, feature selection, artificial bee colony, texture features