Adaptive Score Normalization using Machine Learning for enhanced Fingerprint Recognition

Fingerprint identification systems play a vital role in biometric security but are often influenced by fluctuations in scanning settings and environmental variables. The conventional normalizing techniques such as Min-Max, Z-Score, and Tanh normalization provide fixed solutions that do not adjust to these variations, therefore degrading the accuracy and resilience of the results. This study presents a machine learning-based adaptive normalization system that seamlessly adapts to present circumstances by integrating contextual information, such as ambient variables and scanner-specific features, with raw fingerprint scores. The procedure includes the gathering of data, doing preprocessing, training models using decision trees, random forests, and neural networks, and implementing dynamic normalization. Comprehensive testing conducted on several scanners and settings has shown the efficacy of this method, demonstrating substantial improvements in both recognition accuracy and resilience. Assessment measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score validate the higher efficacy of this adaptive approach in comparison to static methods, thereby improving operational adaptability and establishing a basis for wider biometric identification applications.