Efficient Face Recognition in Real Time for Locating Missing Person

Abstract

Abstract: This paper presents a web-based application system that utilizes deep learning as the core technology of a Face Recognition System, with the primary objective of enhancing the accuracy and efficiency of locating missing individuals. Every day, a significant number of individuals go missing due to various factors such as old age, mental health issues, or conditions like Alzheimer’s. The conventional methods employed for searching for missing persons are typically slow, expensive, and involve protracted physical searches lasting weeks or even months. In contrast, deep learning-based technologies offer a promising solution by rapidly analyzing substantial volumes of data within minutes or hours. By leveraging facial recognition technology, which is an application of deep learning, our proposed system aims to compare images and videos obtained from surveillance cameras with pictures of missing persons to identify potential matches. Specifically, we employ the Resnet deep learning algorithm to examine the images of missing individuals, thereby improving the accuracy and speed of identification and making the process more reliable and efficient. To provide a comprehensive solution, we have developed a user-friendly webbased application system that facilitates the search for missing persons. The application efficiently collects and stores information about missing individuals in a centralized database. Whenever a missing person is identified in a CCTV video stream, our application actively tracks their location. Once the missing person is successfully identified within the video stream, the application promptly sends location details via email to the person’s relative