AI Powered Biometric Authentication: A Deep Learning Approach to Secure Identity Verification

he surge of digital transactions and security concerns has made AI-driven biometric authentication emerge as a contemporary method for performing safe identity verification. This study examines deep learning algorithm integration into bio-metric systems to advance their accuracy and effectiveness and anti-fraud capabilities. Biometric protection systems provide robust security by using personal characteristics consisting of fingerprints alongside facial matching and voice pattern recognition while standard access methods such as passwords and PINs face increased vulnerabilities from cyber threats. Deep learning models particularly including recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have dramatically enhanced biometric system performance through their capacity to decrease incorrect accept or reject decisions. GANs and Siamese Networks have made the system more dependable even when operating with minimal training data. The system faces three critical challenges which include spoofing attacks and privacy issues along with computational complexity problems.