Finding Missing Person Using Machine Learning - Gaikwad Vishal Vijay; Walunj Mauli Parshuram; Gulabsing Dhorkya Vasave; Dr. Suryavanshi A.P (International Journal of Scientific Research in Science and Technology, 2025)
The increasing number of missing person cases worldwide has raised a critical need for intelligent and efficient tracking systems. Traditional methods of identifying and locating missing individuals often rely on manual efforts, which are time-consuming and prone to human error. This project aims to develop a machine learning-based system that automates the process of identifying missing persons using facial recognition and predictive analytics. The system integrates image processing, face matching algorithms, and pattern analysis to identify individuals from databases, CCTV footage, or social media sources. The core idea involves collecting facial data from multiple sources and training a deep learning model capable of recognizing faces across varied conditions such as lighting, angle, and facial expressions. Additionally, the project explores predictive models that can estimate possible locations of missing persons using demographic and behavioral data, improving search efficiency. The proposed system is designed for deployment in collaboration with law enforcement agencies, NGOs, and public databases. By combining AI-based recognition with geospatial data analytics, the system can significantly accelerate the process of locating missing individuals and reunite them with their families in a timely manner. Missing person cases represent a serious global issue affecting millions of families every year. Traditional search and identification techniques depend heavily on manual verification, making them slow, inconsistent, and error-prone. The proposed system leverages the power of machine learning and deep neural networks to automate the process of locating missing individuals through image and video analysis. It uses facial recognition algorithms, data analytics, and cloud-based integration to identify people from large-scale datasets.
Keywords: Machine Learning (ML); Facial Recognition; Convolutional Neural Network (CNN); Feature Extraction; Predictive Analytics; Image Processing; OpenCV; Deep Learning (DL); Transfer Learning; Re-identification Models