Enhanced Fingerprint Recognition system using hybrid Feature Fusion with Deep learning and Machine learning Optimization

Fingerprint recognition has become increasingly critical in biometric authentication systems due to its high reliability and distinctiveness. This research presents a novel hybrid approach combining Histogram of Oriented Gradients (HOG) and Visual Geometry Group 16 (VGG16) deep convolutional neural networks with Principal Component Analysis (PCA) dimensionality reduction and advanced machine learning optimization techniques. We introduce reinforcement learning-based feature selection and ensemble methods to further enhance classification accuracy. Our experimental evaluation on FVC2002 and FVC2004 benchmark datasets demonstrates significant performance improvements, achieving 98.4% accuracy with the combined HOGVGG16 approach. This paper also incorporates generative AI techniques for synthetic fingerprint augmentation and cloud security considerations for biometric system deployment. Our comprehensive analysis shows that the hybrid approach outperforms single-method techniques by 12.1%, establishing new standards for secure and efficient fingerprint recognition in real-world applications.

Keywords: Fingerprint recognition, HOG, VGG16, Deep learning, PCA, Ensemble learning, Reinforcement learning, Generative AI, Biometric authentication.