Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift

Vladimir Zh. Kuklin; Naur Z. Ivanov; Alexander N. Muranov; Islam A. Alexandrov; Elena Yu. Linskaya

doi:10.28991/ESJ-2024-08-06-018

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Vladimir Zh. Kuklin
kuklin_vladimir_ran@mail.ru
Institute of Design and Technology Informatics, Russian Academy of Sciences,, Russian Federation
Naur Z. Ivanov Institute of Design and Technology Informatics, Russian Academy of Sciences,, Russian Federation
Alexander N. Muranov Institute of Design and Technology Informatics, Russian Academy of Sciences,, Russian Federation
Islam A. Alexandrov Institute of Design and Technology Informatics, Russian Academy of Sciences,, Russian Federation
Elena Yu. Linskaya Ivannikov Institute for System Programming, Russian Academy of Sciences,, Russian Federation

Vol. 8 No. 6 (2024): December

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Modern complexes providing biometric identification face several problems, such as information drift caused by the variability of facial patterns, voice timbres, and current states. Information drift can characteristically exhibit short-term (subjects' states have changed) or long-term changes. Simultaneously, the developed trusted systems should not have the properties of explainable AI to prevent the possibility of intruders, based on understanding the system behavior to perform actions to hack the system. This paper's objective is to improve the reliability of biometric authentication by increasing the informativity of the classified images by transforming the correlations between the information features using the Bayes-Minkowski measure. The paper puts forth the proposition of employing neuroimmune models that are founded upon the principles of both acquired and innate immunity, with an analogy to the natural immune system. In addition, the authors propose to analyze correlations between information features instead of the features themselves. To reduce the influence of data drift, the authors suggest using adaptive learning with a teacher and reinforcement, which helps to work even with small and unrepresentative data samples. The proposed algorithm demonstrates a high degree of accuracy, as evidenced by its equal error rate (EER), and is particularly well-suited to feature recognition tasks due to its adaptive model. The test results have shown that the proposed solutions increase the level of security of personal data and improve the reliability of biometric authentication against fraudulent actions of intruders, including approaches based on adversarial algorithms. The integration of the immune structure into the authentication system enables the algorithm to remain stable even when presented with a limited number of samples. The proposed algorithm mitigates the impact of data drift on the authentication outcome.

Doi: 10.28991/ESJ-2024-08-06-018

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Kuklin, V. Z., Ivanov, N. Z., Muranov, A. N., Alexandrov, I. A., & Linskaya, E. Y. (2024). Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift. Emerging Science Journal, 8(6), 2449–2464. https://doi.org/10.28991/ESJ-2024-08-06-018

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Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift

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Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Impediments of Green Finance Adoption System: Linking Economy and Environment

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites