Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the clas...

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Published in:	Acta Informatica Pragensia Vol. 12; no. 1; pp. 3 - 18
Main Authors:	Balogun, Monsurat Omolara, Odeniyi, Latifat Adeola, Omidiora, Elijah Olusola, Olabiyisi, Stephen Olatunde, Falohun, Adeleye Samuel
Format:	Journal Article
Language:	English
Published:	Vysoká škola ekonomická v Praze 01.01.2023 Prague University of Economics and Business
Subjects:	artificial immune system ICT Information and Communications Technologies negative selection algorithm nsa optimized negative selection algorithm recognition accuracy teaching-learning-based optimization algorithm Recognition accuracy Optimized negative selection algorithm Teachinglearningbased optimization algorithm Negative selection algorithm NSA Artificial immune system
ISSN:	1805-4951, 1805-4951
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Abstract	Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system.
AbstractList	Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system.
Author	Falohun, Adeleye Samuel Balogun, Monsurat Omolara Olabiyisi, Stephen Olatunde Omidiora, Elijah Olusola Odeniyi, Latifat Adeola
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Cites_doi	10.1007/978-3-540-89991-4_23 10.1162/jocn.1991.3.1.71 10.1155/2015/198363 10.1166/asl.2014.5458 10.4304/jcp.5.9.1348-1355 10.1016/j.ins.2011.08.006 10.1016/j.patcog.2007.06.035 10.1016/j.proeng.2011.11.2496 10.1007/s11432-011-4323-7 10.1109/TSP.2004.833862 10.48550/arXiv.1905.04717 10.1016/j.jksuci.2019.09.003 10.1016/j.dsp.2004.05.001 10.1109/ICCICCT.2014.6993023 10.1007/s41870-020-00501-0
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Keywords	Recognition accuracy Optimized negative selection algorithm Teachinglearningbased optimization algorithm Negative selection algorithm NSA Artificial immune system
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References	ref13 Sanches (ref12) 2007 ref14 Thakur (ref15) 2017; 4 ref11 ref10 ref0 ref2 ref1 Chikh (ref4) 2017; 6 ref17 ref16 Falohun (ref5) 2013 ref18 ref8 ref7 ref9 ref3 ref6
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SubjectTerms	artificial immune system ICT Information and Communications Technologies negative selection algorithm nsa optimized negative selection algorithm recognition accuracy teaching-learning-based optimization algorithm
Title	Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System
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