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Multimodal Biometric Fusion Techniques for Enhanced Identity Verification in Digital Forensics
Abstract
This chapter explores the integration of multimodal biometric fusion techniques in digital forensics to enhance identity verification accuracy and legal defensibility. Unlike unimodal systems that rely on a single trait, multimodal approaches combine fingerprints, facial recognition, voice, gait, and iris data to reduce false acceptance and rejection rates. The methodology includes deep learning-based feature extraction, score and decision-level fusion strategies, and explainable AI to ensure transparency and legal compliance. Real-world case studies demonstrate up to 29% improvement in match confidence through fusion. The chapter also addresses data governance, privacy preservation, and the necessity for adaptive, interpretable, and ethically sound systems. It concludes by highlighting the potential of AI-driven multimodal biometric frameworks to reshape forensic investigations in the digital era.
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