Enhancing Fingerprint Recognition Using Convolutional Neural Networks: A Robust Approach to Image Reconstruction and Authentication

Reconstructing fingerprints using CNN's is an important aspect of biometric systems. Traditional methods such as small extraction and matching techniques fight loud, distorted fingerprint images. The advent of CNNS provides a promising path to improving the accuracy and robustness of fingerprint recognition systems. CNNs, which can automatically learn hierarchical features, have shown considerable success in a variety of image recognition tasks, making them an attractive option for fingerprint recognition. This article explains how CNN is used to detect fingerprints. This validates the methodology, results, and analysis of the proposed model. The article in this study includes the creation of CNN models for fingerprint reconstruction, performance of the proposed system compared to traditional methods, and analysis of the robustness of CNN-based systems under various conditions. It will be available. The deep learning method is perfect for reconstructing damaged fingerprint images due to poor skin disease, heavy cuts, damp fingers, or bad scans with 98.75% training accuracy and 99.02 % verification accuracy. It has proven ideal.