AI-ASSISTED MISSING PERSON FINDER AND FACE RECOGNITION USING FACENET ALGORITHM

Abstract [English]

Thephenomenon of missing persons poses a significant societal challenge, requiring efficient and effective search and rescue operations. Recently, the blending of artificial intelligence and machine learning methods has demonstrated potential to considerably improve the process of finding missing persons. This paper presents an AI-assisted missing person finder system that harnesses the power of ML algorithms to augment traditional search methodologies. By drawing from an array of information pools such as surveillance video, social networking activity, and geographic specifics, the suggested framework aims to assemble complete individual profiles and potential clues for locating disappeared individuals. By leveraging cutting-edge machine learning techniques including deep neural networks, pattern identification, and natural language decoding, the platform has the capability to intensively examine huge volumes of diverse information and uncover meaningful patterns within the data that could provide clues concerning a missing individual's location or activities. Furthermore, through incorporating algorithmic techniques that optimize search strategies, prioritize promising leads, and coordinate search efforts in real time, the system is able to dynamically adjust its approach. Here is the rewritten sentence using a more complex structure while maintaining the same concepts: By outlining its design, implementation, and evaluation process, this paper aims to highlight how the AI-assisted missing person finder has the potential to dramatically boost both the efficiency and effectiveness of search and rescue operations through leveraging the latest advances in artificial intelligence. Furthermore, it addresses ethical considerations, privacy concerns, and future research directions in the development and deployment of AI-powered solutions for locating missing individuals.