Fingerprint Recognition Revolutionized: Harnessing the Power of Deep Convolutional Neural Networks

Traditional fingerprint recognition systems struggle to manage large datasets and detect certain fingerprint traits. These systems often require manual feature extraction, which slows processing and increases human error. Fingerprint patterns’ complicated ridges and grooves require a method that can efficiently and accurately handle vast amounts of data. Our technique uses Deep Convolutional Neural Networks to address these difficulties. We categorize fingerprints by unique identities, gender, and finger positions to go beyond mere fingerprint matching. This adaptable categorization method improves fingerprint analysis accuracy. Automating and deep learning feature extraction reduces analysis time and human error in our solution. Our ensemble model uses ResNet101, ResNet50, AlexNet, and two exclusive models to improve fingerprint analysis. This ensemble technique uses a softmax layer to combine model predictions and revolutionize biometric security. This integration uses ResNet models’ deep learning and AlexNet's efficient image processing to manage fingerprint data's intricacies, including IDs, finger numbers, and gender. These different insights improve fingerprint classification accuracy and robustness, making the ensemble model useful for advanced security systems and biometric applications. The ensemble model in our study performed SubjectID identification, FingerNum categorization, and Gender recognition with 99.67% to 99.95% accuracy. The range showcases the model's fingerprint analysis precision and efficiency. Our fingerprint categorization across SubjectID, FingerNum, and Gender was more accurate than earlier research. Our ensemble model, which used various deep learning architectures, outperformed standard fingerprint analysis methods.