Acta Informatica Pragensia 2023, 12(1), 3-18 | DOI: 10.18267/j.aip.1863147

Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

Monsurat Omolara Balogun1, Latifat Adeola Odeniyi2, Elijah Olusola Omidiora3, Stephen Olatunde Olabiyisi ORCID...3, Adeleye Samuel Falohun3
1 Faculty of Engineering and Technology, Kwara State University, Ilorin, Kwara State, Federal Republic of Nigeria
2 Department of Computer Science, Chrisland University, Abeokuta, Ogun State, Federal Republic of Nigeria
3 Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Oyo State, Federal Republic of Nigeria

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.

Keywords: Artificial immune system; Negative selection algorithm; Optimized negative selection algorithm; Teaching-learning-based optimization algorithm; Recognition accuracy; NSA.

Received: April 6, 2022; Revised: July 3, 2022; Accepted: July 18, 2022; Prepublished online: July 24, 2022; Published: April 19, 2023  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
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. doi: 10.18267/j.aip.186
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Aiqiang, X., Yong, L., Xiuli, Z., Chunying, Y., & Tingjun, L. (2011). Optimization and Application of Real-Valued Negative Selection Algorithm. Procedia Engineering, 23, 241-246. https://doi.org/10.1016/j.proeng.2011.11.2496 Go to original source...
    2. Aizi, K., & Ouslim, M. (2019). Score level fusion in multi-biometric identification based on zones of interest. Journal of King Saud University - Computer and Information Sciences, 34(1), 1498-1509. https://doi.org/10.1016/j.jksuci.2019.09.003 Go to original source...
    3. Arun R., Sudipta B., Cunjian C., Anuraq C. & Vahid M. R. (2019). Some Research Problems in Biometrics: The Future Beckons. arXiv preprint arXiv:1905.04717. https://doi.org/10.48550/arXiv.1905.04717 Go to original source...
    4. Chen, W., Li, T., Liu, X., & Zhang, B. (2011). A negative selection algorithm based on hierarchical clustering of self set. Science China Information Sciences, 56(8), 1-13. https://doi.org/10.1007/s11432-011-4323-7 Go to original source...
    5. Chikh R & Salim C. (2017). Negative Selection Algorithm: Recent Improvements and its Application in Intrusion Detection System. International Journal of Computing Academic Research, 6(2), 20-30. http://www.meacse.org/ijcar/archives/115.pdf
    6. Falohun A.S., Omidiora E.O., Akanni A.O. & Awofadeju A.S. (2013). Remote Access Control System Using Iris Signature; In Proceedings of the World Congress on Engineering and Computer Science 2013 Vol I., WCECS 2013 (pp. 23-27). IAENG. http://www.iaeng.org/publication/WCECS2013/WCECS2013_pp522-526.pdf
    7. Hira, Z. M., & Gillies, D. F. (2015). A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data. Advances in Bioinformatics, 2015, Article ID 198363. https://doi.org/10.1155/2015/198363 Go to original source...
    8. Kounoudes A., Tsapatsoulis N., Theodosiou Z. & Milis M. (2008). POLYBIO: Multimodal Biometric Data Acquisition Platform and Security System. In Biometrics and identity management (pp. 216-227). Springer. https://doi.org/10.1007/978-3-540-89991-4_23 Go to original source...
    9. Li, G., Li, T., Zeng, J., & Li, H. (2010). An Outlier Robust Negative Selection Algorithm Inspired by Immune Suppression. Journal of Computers, 5(9), 1348-1355. https://doi.org/10.4304/jcp.5.9.1348-1355 Go to original source...
    10. Nath, S. S., Mishra, G., Kar, J., Chakraborty, S., & Dey, N. (2014). A survey of image classification methods and techniques. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 604-607). IEEE. https://doi.org/10.1109/ICCICCT.2014.6993023 Go to original source...
    11. Patil, P., & Jagtap, S. (2020). Multi-modal biometric system using finger knuckle image and retina image with template security using PolyU and DRIVE database. International Journal of Information Technology, 12(4), 1043-1050. https://doi.org/10.1007/s41870-020-00501-0 Go to original source...
    12. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1-15. https://doi.org/10.1016/j.ins.2011.08.006 Go to original source...
    13. Sanches T., Antunes J. & Correia P. L. (2007). A Single Sensor Hand Biometric Multimodal System. In Proceedings of the 15th European signal processing conference (EUSIPCO). IEEE.
    14. Sanderson, C., & Paliwal, K. K. (2004). Identity verification using speech and face information. Digital Signal Processing, 14(5), 449-480. https://doi.org/10.1016/j.dsp.2004.05.001 Go to original source...
    15. Singh, N., Khan, R. A., & Rajshree. (2014). Equal Error Rate and Audio Digitization and Sampling Rate for Speaker Recognition System. Advanced Science Letters, 20(5), 1085-1088. https://doi.org/10.1166/asl.2014.5458 Go to original source...
    16. Thakur, N. & Maheshwari, D. (2017). A Review of Image Classification Techniques. International Research Journal of Engineering and Technology, 4(11), 1588-1591.
    17. Toh, K.-A. ., Jiang, X., & Yau, W.-Y. (2004). Exploiting Global and Local Decisions for Multimodal Biometrics Verification. IEEE Transactions on Signal Processing, 52(10), 3059-3072. https://doi.org/10.1109/tsp.2004.833862 Go to original source...
    18. Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. https://doi.org/10.1162/jocn.1991.3.1.71 Go to original source...
    19. Zhang, T., Li, X., Tao, D., & Yang, J. (2008). Multimodal biometrics using geometry preserving projections. Pattern Recognition, 41(3), 805-813. https://doi.org/10.1016/j.patcog.2007.06.035 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content