Multimodal Deep Hashing Biometric Authentication Systems Based on Neural Networks Regional Applications in Digital IDs

Abstract

With a focus on applications related to digital identities, this paper provides an extensive overview of multimodal deep hashing biometric authentication systems. We lay out precise research goals and examine the most recent approaches, such as privacy-preserving strategies and deep neural architectures. Modern multimodal hashing frameworks are identified, template security and system interoperability issues are evaluated, and future research directions are recommended. We employ a systematic literature search with clear inclusion/exclusion criteria and categorize the works by technique (e.g., CNN, RNN, Transformer), application domain, and modality (e.g., face, fingerprint, iris). We discuss recent developments, including transformer-based biometric models [2][3] and privacy techniques (secure sketches, homomorphic encryption) [4][5]. Key studies are compiled in a standardized comparative table. With an emphasis on open-source platforms (like MOSIP [6][7]), privacy-by-design, and economic effects, we cover policy frameworks (GDPR, eIDAS, and African Union privacy charters) and provide helpful suggestions for implementing digital ID systems in Africa. Future studies and the implementation of safe, privacy-conscious biometrics for identity programs are intended to be guided by our findings.