AI-Powered Image-based Missing Person Identification System

The increase in missing persons has turned into a global problem, burdening the authorities and frequently leaving families in anxiety. Posting circulars and submitting police reports, the traditional search techniques, are typically ineffective and time-consuming. We propose an AI-Powered Image-Based Missing Person Identification System that uses deep learning for quick facial recognition in order to assist with this. The fundamental component of the system is a Convolutional Neural Network (CNN), which is trained on a variety of datasets to recognize and record face features in uploaded photographs. The recorded traits are compared to a database of people who have been reported missing or located. The model reduces false positives and improves search accuracy with its high precision and recall. Docker offers scalable deployment, and Python is used for backend development. Structured data is efficiently stored using PostgreSQL. Without requiring human involvement, the system is designed to automate the search process, including feature extraction, picture preprocessing, and real-time matching. When a possible match is found, real-time alerts are given via email and push notifications. Performance metrics such as F1-score, accuracy, and recall were used for model validation, and the findings show a significant improvement over traditional technique. Informed permission is used to gather photographs, and safe storage and encryption protect data privacy, all of which uphold ethical standards. This study provides a scalable, real-time solution that reunites missing people with their families in addition to demonstrating the effective use of AI in tackling humanitarian challenges.