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September 17, 2025Open Access

AI-Based Missing Person Detection System

SUShubham UpadhyayState Street (United States)DPDishant PatelNorthern Arizona University ASAnil SolankiSawai ManSingh Medical College and Hospital

Key Points

The proposed system significantly reduces the search window for missing persons compared to traditional methods.
Utilizing deep learning and computer vision, the framework automates detection and identification in real-time.
Integration with existing surveillance systems allows for seamless data management and efficient notifications.
Cloud computing enhances the system's scalability and allows for robust data storage without compromising performance.

Abstract

Abstract—Abstract— The rise in urbanization and population growth has resulted in an alarming increase in missing person cases worldwide. Conventional methods of searching, which rely on manual reporting and field investigation, often lead to delayed responses and low success rates. Artificial Intelligence (AI), par- ticularly computer vision and deep learning, provides powerful tools for automating detection, identification, and tracking of missing individuals. This paper proposes an AI-based missing person detection system that leverages image recognition, face matching, and surveillance integration to assist authorities and families in locating missing persons efficiently. The system utilizes Convolutional Neural Networks (CNNs), transfer learning models such as VGGFace and FaceNet, and integrates with existing CCTV networks to provide real-time alerts. Our implementation is built using Python with Flask as the backend framework and a web interface designed with HTML, CSS, and JavaScript. The system supports real-time notifications, database integration, and user-friendly access through a web app, ensuring practicality and scalability. In addition, the system is designed to allow flexible data management where authorities can continuously update records, thereby improving accuracy over time. It ensures compatibility with multiple camera sources, making it adaptable for both small-scale and citywide deployments. The integra- tion of cloud services enhances scalability and allows storage of large datasets without compromising speed. Moreover, the system incorporates preprocessing techniques to handle varying image qualities, ensuring robustness in diverse environments. Ethical considerations, including data privacy and responsible AI practices, are also discussed to guarantee safe usage. Through experimental results and analysis, this research demonstrates that an AI-assisted framework can drastically reduce the search window for missing individuals compared to manual approaches. The combination of automated recognition, easy accessibility, and scalable architecture ensures that the proposed solution has the potential to be adopted in real-world scenarios and assist both local authorities and large organizations working in public safety.Terms— Artificial Intelligence, Computer Vision, Deep Learning, Missing Person Detection, Facial Recognition, Surveillance Systems, Flask, Python, Web Application, Cloud Computing, Ethical AI, Real-Time Processing Index Terms—Artificial Intelligence, Computer Vision, Deep Learning, Missing Person Detection, Facial Recognition, Surveil- lance Systems, Flask, Python, Web Application

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