Advanced Fingerprint Authentication System and Neurodegenerative Disorder Multi-modal Pattern Recognition Techniques Using Deep Learning
Sajol Debnath, K. M. Danial Sadad, Md. Shamim Parvej, Shaik Abdul Kareem, Kaysun Molla, Anindya Nag · River Publishers eBooks · 2025
Biometric fingerprint identification systems are critical for improving security across a variety of businesses; nevertheless, single biometric approaches frequently have limits due to non-universality and noise, resulting in lower genuine acceptance rates (GAR) and greater false acceptance rates (FAR). To overcome these issues, this work investigates a multimodal biometric system that integrates fingerprint data with other biometric traits to improve accuracy and resistance to spoofing. We create a multimodal pattern recognition framework with deep learning models like EfficientNet (89%), WideResNet (94%), Xception (83%), DenseNet (91%), and CNN (88%), with WideResNet attaining the highest classification accuracy. Beyond security, generative AI applications within this framework have the potential to advance neurodegenerative illness research by allowing for the analysis of biometric data to diagnose and monitor cognitive decline early on. This method dramatically improves biometric systems’ resilience, security, and dependability, making it appropriate for both high-security situations and noninvasive neuropsychological examinations.