Towards biometric recognition system based on explainable classifier methods

Abstract

er the past decade, biometric systems have advanced significantly, achieving high classifica tion accuracy and minimal equal error rates (EER). However, many conventional methods lack transparency and explainability, which are critical in areas like security and identity verifica tion, where trust is paramount. This limitation restricts the ability to understand these systems’ decision-making processes, making it difficult to ensure reliability and accountability in sensi tive applications. To address these challenges, we propose the development of an efficient biometric system based on explainable, rule-based classifiers. Unlike traditional approaches, our method incorpo rates explainability at its core, offering clear insights into the system’s decision-making process while maintaining high performance. This approach ensures that the system is not only accurate but also adaptable and user-friendly, enabling its application across a range of classification and predictive tasks. By prioritizing transparency alongside performance, the proposed system aims to meet the growing demand for trust and usability in biometric applications. Its dual focus on achieving low EER and delivering explainable outcomes ensures it is suitable for deployment in critical domains. This balance between accuracy and explainability positions the system as a reliable and advanced solution for high-stakes environments like security and identity management