RESEARCH AND DEVELOPMENT OF BIOMETRIC METHODS

Identification systems play a very important role in today's society. Complex security requirements have prompted experts to explore ways in which biometrics can be used to identify customers. In this article, the concept of biometrics, biometric data and its variants are considered. The purpose of the article is to study the process of identification, to produce its improved version and introduce innovation. Thus, the following points served as a research method:
- Initialization.
- Fitness functions.
- Unclear clusterleu method.
The results showed that the advancement of biometric systems and biometric sensors can improve the identity and prevent others from using the identity, the system has great potential to improve the security and accuracy of the biometric technology system. Biometric systems increase the security of users, as well as ensure accuracy in the identification of personal identity. Thus, the accuracy of the proposed method is compared with four modern methods. The comparison shows that the proposed approach provided a high accuracy of about 99.89% and a low error rate of 0.18%. It turns out that there is real potential for the integration of fingerprints and iris biometrics in many subjects with the appropriate assessment.

1. Aas K.F. (2006) The body does not lie : Identity, risk and trust in technoculture, Crime, Media, Culture, 2(2), pp. 143–158. https://doi.org/10.1177/1741659006065401.

2. Abdurrahim S.H., Samad S.A. and Huddin A.B. (2017) Review on the effects of age, gender, and race demographics on automatic face recognition, The Visual Computer. https://doi.org/10.1007/s00371-017-1428-z. Akrich M. (1992) The de-scription of technical objects, in: W. Bijker and J. Law (Eds) Shaping Technology/Building Society. Studies in Sociotechnical Change, pp. 205–224 (Cambridge: MIT Press), [Google Scholar] Amoore L. (2006) Biometric borders: Governing mobilities in the war on terror, Political Geography, 25(3), pp. 336–351. https://doi.org/10.1016/j.polgeo.2006.02.001 [Crossref], [Web of Science ®], [Google Scholar]

3. Beveridge J.R., Givens G.H., Phillips P.J. and Draper B.A. (2009) Factors that influence algorithm performance in the face recognition grand challenge, Computer Vision and Image Understanding, 113(6), pp. 750–762. https://doi.org/10.1016/j.cviu.2008.12.007 [Crossref], [Web of Science ®], [Google Scholar]

4. Bowyer K.W., Hollingsworth K. and Flynn P.J. (2008) Image understanding for iris biometrics: A survey, Computer Vision and Image Understanding, 110(2), pp. 281–307. https://doi.org/10.1016/j.cviu.2007.08.005 [Crossref], [Web of Science ®], [Google Scholar]

5. Breckenridge K. (2005) The biometric state: The promise and peril of digital government in the new South Africa, Journal of Southern African Studies, 31(2), pp. 267–282. https://doi.org/10.1080/03057070500109458 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]

6. Callon M. (1984) Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay, The Sociological Review, 32(1_suppl), pp. 196–233. [Google Scholar]

7. De Marsico M., Nappi M., Riccio D. and Wechsler H. (2013) Demographics versus biometric automatic interoperability, in: International Conference on Image Analysis and Processing, pp. 472–481 (Berlin: Springer), [Google Scholar]

8. Gelb A. and Clark J. (2013) Identification for development: The biometrics revolution. CGD Working Paper 315 (Washington DC: Center for Global Development), [Google Scholar]

9. Grommé F. (2015) Turning aggression into an object of intervention: Tinkering in a Crime Control Pilot Study, Science as Culture 24(2), pp. 227–247. https://doi.org/10.1080/09505431.2014.992331 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]

10. Howard J.J. and Etter D. (2013) The effect of ethnicity, gender, eye color and wavelength on the biometric menagerie, 2013 IEEE International Conference on Technologies for Homeland Security (HST), IEEE. [Google Scholar]

11. Introna L. (2005) Disclosive ethics and information technology: Disclosing facial recognition systems, Ethics and Information Technology, 7(2), pp. 75–86. https://doi.org/10.1007/s10676-005-4583-2 [Crossref], [Google Scholar]

12. Introna L. and Nissenbaum H. (2010) Facial Recognition Technology: A Survey of Policy and Implementation Issues (New York: Center for Catastrophe Preparedness and Response, New York University). [Google Scholar] Introna L. and Wood D. (2004) Picturing algorithmic surveillance: The politics of facial recognition systems, Surveillance & Society, 2(2/3), pp. 177–198. [Google Scholar]

13. Jacobsen E. K. U. (2012) Unique Identification: Inclusion and Surveillance in the Indian biometric assemblage, Security Dialogue, 43(5), pp. 457–474. https://doi.org/10.1177/0967010612458336 [Crossref], [Web of Science ®], [Google Scholar]

14. Tong Z., Ye F., Yan M., Liu H. & Basodi S. (2021) A survey on algorithms for intelligent computing and smart city applications. Big Data Min. Anal. 4(3), pp. 155–172. https://doi.org/10.26599/BDMA.2020.9020029.

15. Huang H. et al. (2020) Machine learning-based multi-modal information perception for soft robotic hands. Tsinghua Sci. Technol. 25(2), pp. 255–269. https://doi.org/10.26599/TST.2019.9010009.

16. Pang J., Huang Y., Xie Z., Li J. & Cai Z. (2021) Collaborative city digital twin for the COVID-19 pandemic: a federated learning solution. Tsinghua Sci. Technol. 26(5), pp. 759–771. https://doi.org/10.26599/TST.2021.9010026.

17. Mabrouki J., Azrour M., Fattah G., Dhiba D. & Hajjaji S.E. (2021) Intelligent monitoring system for biogas detection based on the Internet of Things: Mohammedia, Morocco city landfill case. Big Data Min. Anal. 4(1), pp. 10–17.