AI-Powered Missing Person Identification System Using Computer Vision

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

I developed an AI-powered Missing Person Identification System that explores how computer vision can help compare facial features and support missing-person search efforts.

The project uses MediaPipe Face Landmarker to extract facial landmarks and a K-Nearest Neighbors (KNN) classifier to compare feature representations. The goal is to explore how machine learning can assist in identifying potential matches.

This is a prototype, not a verified identification service. Any potential match requires human review and independent verification.

GitHub: https://github.com/Shrutirowlo/missing_person_AI

Demo

Source code: https://github.com/Shrutirowlo/missing_person_AI

The repository contains the project implementation. Please refer to the README for setup instructions and available demonstrations.

Code

GitHub repository: https://github.com/Shrutirowlo/missing_person_AI

The repository contains the source code for the missing-person identification prototype. Please refer to the repository for implementation details and setup instructions.

How I Built It

The system explores a computer vision pipeline using MediaPipe Face Landmarker for facial landmark extraction and K-Nearest Neighbors (KNN) for classification.

The pipeline extracts facial landmark features from images, prepares feature vectors for training, and evaluates the classifier on held-out samples.

The project also explores traditional face-recognition approaches, including Fisherfaces and LBPH.

The implementation uses Python and machine-learning tools. See the repository for the exact dependencies and implementation details.

Why Does Open Innovation Matter?

Open-source computer vision and machine-learning tools make it possible for developers and researchers to inspect implementations, experiment with different approaches, and learn without depending entirely on proprietary APIs.

For a missing-person identification prototype, local processing can also help reduce the need to upload sensitive facial images to third-party AI services.

However, facial recognition can produce false matches and raise privacy concerns. This prototype should support research and human review, not make definitive identity claims or replace official missing-person procedures.