Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System
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Date
2023
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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.
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Citation
9. Balogun, M. O., Odeniyi, L. A., Omidiora, E. O., Olabiyisi, S. O., & Falohun, A. S. (2023). Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System. Acta Informatica Pragensia, 12(1), 3-18.