Enhancing Biometric Accuracy: Dynamic Thresholds and Breast Temperature Analysis for Medical Consultations

The current biometrics article combines significant technological advancements with expanding practical applications, tackling ethical and security challenges. Machine learning and deep learning improvements have significantly increased the accuracy, efficiency, and security of biometric systems. Nonetheless, the field faces issues regarding privacy, ethics, and fairness, underscoring the necessity to balance security with user convenience. Research highlights the importance of balancing the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) for the effectiveness of biometric systems. A low FAR is crucial for security, while a low FRR ensures a positive user experience. Machine learning algorithms promise dynamic decision threshold adjustments based on context and user behavior, enhancing biometric system performance. These algorithms enable systems to adapt to data variations and contextual shifts, optimizing identification or authentication accuracy and efficiency. By analyzing data patterns, algorithms can finely adjust thresholds to achieve a balance between FAR and FRR, integrating security with convenience. This smart adjustment approach boosts the reliability and overall performance of biometric systems, meeting specific user needs and application scenarios.