Multimodal Biometric Authentication: Integrating Fingerprints, Face, and Voice Using AI

As digital security threats grow increasingly sophisticated, the need for more robust and user-friendly authentication systems becomes critical. Multimodal biometric authentication, leveraging multiple biometric traits such as fingerprints, facial recognition, and voice patterns, offers a comprehensive and reliable security solution. This paper explores how artificial intelligence (AI), particularly machine learning and deep learning algorithms, enhances the accuracy, adaptability, and resilience of multimodal biometric systems. By integrating data from diverse biometric sources, AI can mitigate the weaknesses of single-modality systems, reduce false acceptance and rejection rates, and improve performance under varying environmental conditions. This study also examines real-world applications, system architecture, and the challenges of data fusion, privacy, and computational efficiency. The research highlights how multimodal AI-driven systems are shaping the future of secure identity verification across industries such as finance, healthcare, and border control.