A Smart Barcode Identifier & Recovery System for Lost Individuals in Mass Gatherings

Abstract

Mass public events such as pilgrimages, festivals, and large community gatherings frequently lead to individuals—especially children and elderly people—becoming separated from their families. Conventional recovery methods are often slow, inefficient, and emotionally distressing. To overcome these challenges, this paper proposes an AI-driven Smart Barcode-Based Identification and Recovery System that integrates machine learning, computer vision, and facial recognition for real-time and automated identification. Each participant is provided with a barcode or QR code–embedded wristband linked to a secure digital profile containing personal details, emergency contacts, and medical information. Authorized personnel can scan the codes using a mobile application enhanced with AI-based identity verification and deep learning techniques. Machine learning pipelines improve identification accuracy through pattern recognition, anomaly detection, and predictive matching, thereby minimizing human error.
The proposed intelligent recovery framework ensures fast, secure, and scalable operations by integrating cloud computing, data analytics, and automated decision-making. Predictive AI models and automated alert mechanisms significantly enhance response time, improve crowd management, strengthen emergency coordination, and provide a reliable, data-driven safety infrastructure for mass public events.