Missing Person Finder Using Deep Learning

Abstract

The increasing number of Missing persons in public spaces such as train stations, airplane terminals, and malls presents a significant challenge for law enforcement agencies and security personnel. In response to this challenge, we present our solution an innovative system that utilizes cutting-edge machine learning and computer vision techniques, specifically deep learning. Our proposed system aims to address the task of locating missing individuals within vast collections of recorded or live video feeds. Our solution focuses on the development of a robust face detection system capable of accurately identifying facial features using a query image. By employing state-of-the-art facial recognition technology, our system aims to determine the precise time and location of the missing person within the video footage. To ensure the reliability and effectiveness of our solution, we will train the deep learning model on a comprehensive dataset comprising diverse facial images. This dataset will encompass variations in lighting conditions, facial expressions, poses, and demographics, thus enabling our system to handle real-world scenarios effectively. The system will integrate cutting-edge deep learning algorithms to extract and analyze facial features, enabling the identification of missing persons with high accuracy and efficiency. The implementation of deep learning techniques empowers the system to automatically learn and adapt to different facial patterns and attributes, enhancing its overall performance. Our system will also incorporate real-time video processing capabilities, allowing it to operate on both pre-recorded video feeds and live surveillance streams. This feature will enable authorities to swiftly respond to missing person reports, facilitating rapid search and rescue efforts.