Exploring the Intersection of AI, Biometrics, and IoT: Enhancing Security and User Experience

Abstract: The convergence of Artificial Intelligence (AI), Biometrics, and the Internet of Things (IoT) heralds a new era in security and user authentication. This comprehensive review delves into the symbiotic relationship between these domains, highlighting their pivotal role in enhancing IoT security and user experience. From fingerprint and facial recognition technologies to AI-powered biometric systems, this exploration uncovers the transformative potential and challenges in this dynamic landscape.

Introduction: Biometrics, encompassing unique human traits for access control, has emerged as a cornerstone in secure authentication. The rapid evolution of IoT necessitates robust security measures, driving the integration of AI-powered biometrics to fortify authentication protocols and mitigate vulnerabilities.

Review Methodology: A meticulous analysis of literature, encompassing research articles and conference papers, forms the foundation of this review. Methodological rigor ensures comprehensive insights into AI, biometrics, and IoT integration across diverse applications.

AI-Powered Biometrics in IoT Security: The synergy between AI algorithms and biometric modalities empowers IoT security by enabling advanced pattern recognition, anomaly detection, and adaptive decision-making. This fusion not only enhances security but also augments user convenience and privacy.

Challenges and Future Directions: While AI-powered biometrics offer immense potential, challenges such as interoperability, ethical considerations, and data privacy remain pertinent. Future research must address these hurdles to unlock the full capabilities of this transformative technology.

Conclusion: The triad of AI, biometrics, and IoT heralds a paradigm shift in digital security and user authentication. By delving into the interplay of these domains, this review elucidates pathways for innovation, emphasizing the imperative of robust security frameworks in an increasingly interconnected world.

CRediT Authorship Contribution Statement: Ali Ismail Awad: Conceptualization, Investigation, Methodology, Project Administration, Visualization, Writing – Original Draft, Writing – Review & Editing.
Aiswarya Babu: Data Curation, Investigation, Methodology, Visualization, Writing – Original Draft, Writing – Review & Editing.
Ezedin Barka: Investigation, Writing – Original Draft, Writing – Review & Editing.
Khaled Shuaib: Investigation, Writing – Original Draft, Writing – Review & Editing.

Declaration of Competing Interest: The authors declare no competing financial interests or personal relationships that could influence the work reported in this paper.

Acknowledgments: The authors express gratitude to anonymous reviewers for valuable feedback. This work was supported by a joint research grant between UAEU and ZU under the Big Data Analytics Center, UAEU, UAE (Grant No. 12R141).