Enhanced Biometric Authentication System using Deep Learning-based Multimodal Fusion

Biometric authentication is a popular way of secure identity verification, but unimodal systems are plagued by weaknesses like spoofing attacks, environmental noise, and inconsistencies in data. To overcome these weaknesses, this study suggests an Enhanced Biometric Authentication System with Deep Learning-Based Multimodal Fusion, which combines multiple biometric features to enhance accuracy, security, and robustness. Through the application of deep learning frameworks, the system extracts and combines features of varied biometric modalities for boosting discrimination capability as well as hindering illegitimate access. It not only improves upon the drawbacks of unimodal authentication but also enhances responsiveness to practical fluctuations. Experimental evaluations point out enhanced recognition accuracy, adversarial resilience, as well as efficacy in identity validation. The results indicate that multimodal deep learning methods provide a more robust and scalable solution for contemporary authentication systems, and they can be potentially applied to cybersecurity, finance, and access control.