• M Mounika
• Mohan R
• Kiran Kumar M
• Daruru Tharaka Rathna
• K Padma Priya

The rising number of missing persons, especially children, the elderly, and individuals with mental health conditions, poses a growing global concern requiring efficient and timely intervention. Traditional manual tracking methods often fall short due to delays, human error, and limited scalability. Recent advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have revolutionized surveillance and identification systems, offering promising solutions to this challenge. This review explores comprehensive, AI-driven frameworks that integrate facial recognition, real-time surveillance analysis, and secure data management to enhance the identification and tracking of missing individuals. Techniques such as Convolutional Neural Networks (CNN), Multitask Cascaded Convolutional Networks (MTCNN), FaceNet, Local Binary Pattern Histogram (LBPH), and K-Nearest Neighbors (KNN) are employed for accurate face detection, feature extraction, and classification, even under challenging conditions like low lighting or crowded environments. Advanced features such as multi-camera feed analysis, geo-fencing, and 3D facial reconstruction using models like Shade-GAN further improve identification precision. Secure, user-friendly platforms enable public participation and law enforcement coordination through real-time alerts, data uploads, and live CCTV monitoring. By minimizing human dependency and enhancing response time, these AI-enabled systems significantly improve the effectiveness of missing persons recovery efforts and provide a scalable, privacy-conscious solution for public safety and law enforcement. This paper provides a consolidated view of existing AI-based systems, highlights their strengths and serves as a foundation for future research and development of more efficient, ethical, and robust missing persons identification frameworks.

Convolutional Neural Networks (CNN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), 3D Reconstruction, GAN, Computer Vision, Haar Cascades, Local Binary Pattern Histogram (LBPH).

"A Survey Paper on Missing Person Identification System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b334-b341, May-2025, Available :http://www.jetir.org/papers/JETIR2505139.pdf

"A Survey Paper on Missing Person Identification System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb334-b341, May-2025, Available at : http://www.jetir.org/papers/JETIR2505139.pdf