Image-based Missing Child Reclamation using Deep Learning

Every year, millions of children go missing, leaving families distressed and with little hope of recovery. Traditional methods for searching missing children mainly rely on manual efforts, which are often slow and ineffective. This paper proposes an advanced solution using Deep Learning (DL) and Convolutional Neural Networks for faster and more accurate identification of missing children. The proposed system can capture facial features from CCTV footage, social media platforms, and public databases and match them with the pre-registered images of missing children. By integrating face recognition technology with real-time data collection, the system can significantly improve the process of tracking and reclaiming missing children. The model is designed to handle various challenges such as changes in facial features, aging, and partial obstructions. Extensive testing of the model showed an accuracy of over 92%, demonstrating the feasibility of using deep learning for rapid child reclamation.