AI Platform for Missing Children and Human Trafficking Detection

Abstract

Human trafficking and child abduction remain critical global challenges due to fragmented data systems and the lack of real-time monitoring tools. Traditional search processes involving manual record verification and public alerts are often time-consuming and inefficient. This project proposes an AI-driven platform for Missing Children and Human Trafficking Detection, integrating facial recognition, pattern analysis, and natural language processing (NLP) to enable proactive victim identification. The system unifies multiple data sources — including CCTV feeds, FIR records, travel logs, and social media intelligence — into a centralized web portal. Using a VGG-Face-based Convolutional Neural Network (CNN) for deep facial feature extraction and a K-Nearest Neighbor (KNN) classifier for efficient matching, the platform automatically identifies missing individuals or traffickers across surveillance networks. Once a match is found, the system sends real-time alerts to concerned authorities and families. The model demonstrates high accuracy even under variations in lighting, pose, and image quality. The proposed framework enhances the speed, accuracy, and scalability of investigations, offering an effective AI-assisted approach for law enforcement and social welfare agencies. Future extensions include mobile integration, predictive route analytics, and national-level deployment for cross-border trafficking prevention.