EFFICIENT MISSING CHILD DETECTION USING DEEP LEARNING AND INTELLIGENT SVM CLASSIFICATION

Abstract

The increasing number of missing child cases across the world has become a major social and security concern, demanding efficient and intelligent identification systems for rapid child recovery. Traditional child identification methods rely heavily on manual investigation, public reporting, and human surveillance, which are often time-consuming and less effective in crowded environments. To address these challenges, this paper proposes an efficient missing child detection system using deep learning and intelligent Support Vector Machine (SVM) classification techniques. The proposed framework utilizes facial recognition technology combined with deep learning algorithms to automatically detect and identify missing children from images and video surveillance data. Convolutional Neural Networks (CNNs) are employed for feature extraction and facial representation learning, while a multiclass Support Vector Machine (SVM) classifier is used for accurate child identification and classification. The system processes facial images collected from surveillance cameras, public datasets, and uploaded records to compare them with stored missing child databases in real time. Image preprocessing techniques such as face detection, normalization, resizing, and feature enhancement are applied to improve recognition accuracy under varying environmental conditions including lighting variations, occlusions, and facial pose differences. The integration of deep learning with intelligent SVM classification enhances detection precision, reduces false identification rates, and improves computational efficiency. Experimental analysis demonstrates that the proposed system achieves high accuracy, fast response time, and reliable real-time performance compared to conventional identification approaches. The proposed framework can assist law enforcement agencies, child protection organizations, and smart surveillance systems in rapidly identifying missing children and improving public safety. Furthermore, the system supports scalable deployment in crowded public areas such as railway stations, airports, shopping malls, and educational institutions. Overall, the proposed intelligent identification system provides an effective, automated, and scalable solution for missing child detection and recovery.