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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | ces |
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Prague University of Economics and Business
2023-04-01
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| Series: | Acta Informatica Pragensia |
| Subjects: | |
| Online Access: | https://aip.vse.cz/artkey/aip-202301-0002_optimized-negative-selection-algorithm-for-image-classification-in-multimodal-biometric-system.php |
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| author | Monsurat Omolara Balogun Latifat Adeola Odeniyi Elijah Olusola Omidiora Stephen Olatunde Olabiyisi Adeleye Samuel Falohun |
| author_facet | Monsurat Omolara Balogun Latifat Adeola Odeniyi Elijah Olusola Omidiora Stephen Olatunde Olabiyisi Adeleye Samuel Falohun |
| author_sort | Monsurat Omolara Balogun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T16:18:02Z |
| format | Article |
| id | doaj.art-49bd981510444fb987412e47ae866fa9 |
| institution | Directory Open Access Journal |
| issn | 1805-4951 |
| language | ces |
| last_indexed | 2024-04-09T16:18:02Z |
| publishDate | 2023-04-01 |
| publisher | Prague University of Economics and Business |
| record_format | Article |
| series | Acta Informatica Pragensia |
| spelling | doaj.art-49bd981510444fb987412e47ae866fa92023-04-23T21:32:45ZcesPrague University of Economics and BusinessActa Informatica Pragensia1805-49512023-04-0112131810.18267/j.aip.186aip-202301-0002Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric SystemMonsurat Omolara Balogun0Latifat Adeola Odeniyi1Elijah Olusola Omidiora2Stephen Olatunde Olabiyisi3Adeleye Samuel Falohun4Faculty of Engineering and Technology, Kwara State University, Ilorin, Kwara State, Federal Republic of NigeriaDepartment of Computer Science, Chrisland University, Abeokuta, Ogun State, Federal Republic of NigeriaFaculty of Engineering and Technology, Ladoke Akintola University of Technology, Oyo State, Federal Republic of NigeriaFaculty of Engineering and Technology, Ladoke Akintola University of Technology, Oyo State, Federal Republic of NigeriaFaculty of Engineering and Technology, Ladoke Akintola University of Technology, Oyo State, Federal Republic of NigeriaClassification 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.https://aip.vse.cz/artkey/aip-202301-0002_optimized-negative-selection-algorithm-for-image-classification-in-multimodal-biometric-system.phpartificial immune systemnegative selection algorithmoptimized negative selection algorithmteaching-learning-based optimization algorithmrecognition accuracynsa |
| spellingShingle | Monsurat Omolara Balogun Latifat Adeola Odeniyi Elijah Olusola Omidiora Stephen Olatunde Olabiyisi Adeleye Samuel Falohun Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System Acta Informatica Pragensia artificial immune system negative selection algorithm optimized negative selection algorithm teaching-learning-based optimization algorithm recognition accuracy nsa |
| title | Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System |
| title_full | Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System |
| title_fullStr | Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System |
| title_full_unstemmed | Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System |
| title_short | Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System |
| title_sort | optimized negative selection algorithm for image classification in multimodal biometric system |
| topic | artificial immune system negative selection algorithm optimized negative selection algorithm teaching-learning-based optimization algorithm recognition accuracy nsa |
| url | https://aip.vse.cz/artkey/aip-202301-0002_optimized-negative-selection-algorithm-for-image-classification-in-multimodal-biometric-system.php |
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