Abstract: The rising number of missing child cases globally highlights the urgent need for a more efficient and intelligent identification and recovery system. Conventional methods, including manual tracking and public awareness initiatives, often fall short due to time limitations and insufficient data management. This research proposes a comprehensive Missing Child Identification System that leverages deep learning, facial recognition, and big data analytics to enhance identification accuracy and operational efficiency. By employing convolutional neural networks (CNNs) and transfer learning, the system compares images of missing children with those in existing databases and surveillance footage. It also incorporates biometric data, such as facial embeddings and age progression algorithms, to adapt to changes in appearance over time. Additionally, the system features an AI-powered alert mechanism that promptly notifies law enforcement and relevant authorities when a match is identified. Real-time analysis and pattern recognition capabilities significantly reduce search times and improve recovery rates. The system’s scalable architecture allows seamless integration with existing surveillance networks and law enforcement databases, making it a viable solution for large-scale deployments. Furthermore, the implementation of privacy-preserving techniques ensures data security and compliance with legal standards. Experimental evaluations validate the system's effectiveness, demonstrating robust performance with high precision and recall rates in diverse scenarios. This study presents a scalable and intelligent solution designed to expedite the recovery of missing children, addressing the limitations of traditional investigative approaches while offering a proactive and efficient response mechanism.
Keywords: Missing Child Identification, Facial Recognition, Deep Learning, Convolutional Neural Networks, Transfer Learning, AI-Powered Alert System, Child Recovery System.
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DOI:
10.17148/IJARCCE.2025.14350
