Enhancing Cybersecurity with Biometric Systems and Blockchain Integration

Abstract

Strong and safe identity management systems are essential in the age of digital transformation to combat growing cybersecurity risks including impersonation, identity theft, and illegal data breaches. Due to their reliance on centralized databases, traditional identity management frameworks are vulnerable to issues including privacy problems and single points of failure. This study suggests a novel method of safe and decentralized identity management by combining blockchain technology with OpenCV-based biometric systems to address these issues. Blockchain technology offers a dependable method for managing and storing identification data because of its decentralized architecture, immutability, and tamper-proof characteristics. At the same time, OpenCV, a popular open-source computer vision library, provides strong biometric data processing tools like iris detection, fingerprint matching, and facial recognition. When combined, these technologies produce. The conceptual framework, system architecture, and implementation method for integrating blockchain and OpenCV-based biometric systems are explored in this study. It outlines several important benefits, including as improved authentication procedures, decentralized data storage, and defense against online threats. The study also examines practical uses in a range of industries, including government systems, healthcare, and financial services, where this integration can greatly strengthen cybersecurity defenses. The paper also discusses potential implementation obstacles, including regulatory compliance, computing overhead, and scalability issues. This study attempts to aid in the creation of next-generation identity management solutions that put security, effectiveness, and user privacy first by examining the technological trade-offs and security benefits.