AI-Based Missing Person Identification and Tracking System

Dr. Chandrakanth G Pujari, Kannika D, H Kavana, Kavya R

Finding a missing person is a race against the clock, yet in most jurisdictions the process still depends heavily on manual effort: officers dig through paper case files, pass around printed photographs, and lean on personal memory to connect a fresh sighting with an older report. As the number of open cases piles up, this kind of manual cross-checking only gets slower and less dependable, and a promising lead can slip through simply because nobody happened to place two particular photographs side by side. This report looks at a software system built to automate that comparison step, using computer-visionbased face geometry rather than raw image matching. Every photograph that enters the system, whether it comes attached to a missing-person case or a public sighting, is run through a 468-point three-dimensional facial landmark detector, which produces a fixed-length numeric signature for the face. A distance-weighted K-Nearest Neighbour classifier then checks new signatures against the pool of open cases and flags any pair that falls within a configurable similarity threshold, automatically closing the case and emailing the reporting family. The system keeps its two user groups on separate interfaces: a role-gated portal for police officers and administrators, and an open public portal that lets any bystander submit a sighting without creating an account. Case and image data live in a local SQLite store, and an interactive map lets administrators see how open cases are spread across cities. Because the core face-landmarker model only needs to be downloaded once and is then cached, the system keeps working without an active internet connection after initial setup, which matters for stations in areas with patchy connectivity.