Smart Surveillance for Finding Missing Person

– The identification of missing persons remains one of the most challenging tasks for law enforcement and humanitarian organizations worldwide. Traditional methods, which rely heavily on manual visual inspection and outdated biometric data, are often time-consuming, inefficient, and prone to human error. In recent years, the rapid advancement of artificial intelligence (AI), particularly deep learning, has shown significant potential in automating and improving the accuracy of missing person identification. This research explores the integration of deep learning methodologies—specifically convolutional neural networks (CNNs), facial recognition systems, and generative adversarial networks (GANs)—to develop a robust framework for identifying missing individuals from diverse and dynamic sources, such as surveillance footage, social media images, and official databases. This study concludes that deep learning offers a transformative approach to missing person identification by automating recognition tasks, improving matching precision, and enabling scalability across large datasets. It also highlights the ethical and privacy concerns involved in deploying AI-based surveillance systems and advocates for the responsible use of biometric technologies. Future work includes expanding the system to support multimodal data (e.g., voice, gait, text), crossborder interoperability, and integration with mobile applications for broader public engagement in locating missing persons.