<![CDATA[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 Full Text: PDF]]>
