Volume 11 • Issue 1 • PP: 06–13 • 2026
A Systematic Literature Review on the Integration of Computer Vision and IoT Technologies for Enhancing Voter Verification Accuracy in Electoral Systems
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The rapid evolution of digital technologies has transformed how societies manage sensitive information and authenticate identity in critical systems. Within the domain of cybersecurity and artificial intelligence (AI), the integration of computer vision and Internet of Things (IoT) technologies has emerged as a promising approach to improving real-time data verification and process automation. This systematic literature review examines how computer vision and IoT technologies can be jointly leveraged to enhance voter verification accuracy in electoral systems. Following the PRISMA 2020 guidelines, the review systematically searched four academic databases, identifying 351 initial studies. After rigorous screening based on predefined inclusion and exclusion criteria, 15 studies were selected for comprehensive analysis. The findings reveal three major themes: (1) emerging technical architectures combining biometric authentication with blockchain-based verification, (2) performance outcomes demonstrating high accuracy rates (97–100%) in controlled environments, and (3) persistent challenges in scalability, real-world deployment, and security against sophisticated AI-enabled attacks such as deepfakes. While the PRISMA process was conducted in full, the limited scope of the project, compressed timeline, and restricted access to paywalled articles likely influenced the depth and completeness of the synthesis. Nevertheless, the review provides structured insight into current implementation approaches, technical methods, and research gaps, with particular relevance to contexts like Uzbekistan where recent OSCE ODIHR election observation reports have documented systemic weaknesses in voter verification and turnout reporting.
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References
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