Fingerprint Recognition System based on Artificial Neural Networks integrated with Machine Learning

This study represents a fingerprint recognition approach that combines Artificial Neural Networks (ANN) with Machine Learning (ML) approaches to improve biometric identification in terms of precision, efficiency, and dependability. Due to its distinctiveness and permanence, fingerprint recognition is a widely adopted biometric authentication method in security systems. However, challenges such as noise, partial prints, and varying environmental conditions often degrade performance. system begins by preprocessing fingerprint images to reduce noise and extract distinctive features, such as ridges and minutiae points. These topographies are then used as inputs to a deep neural network model trained on labelled data set of fingerprint samples. ANN architecture is optimized to recognize and classify fingerprint patterns accurately, even when presented with partial or low-quality images. To strengthen system robustness, Machine Learning strategies such as data augmentation are utilized, leading to enhanced performance and dependability. Through extensive testing, our system demonstrates high accuracy in correctly identifying individuals across a variety of conditions, with an emphasis on minimizing false positives and negatives. This fingerprint recognition system has potential applications in secure access control, identity verification, and law enforcement, offering a highly reliable, scalable, and efficient solution for biometric security.