Finding Missing Person Based on Face Recognition Using AI in Video Surveillance System

Abstract

Abstract: AI-based face recognition for missing person searches is a promising strategy that has the potential to significantly speed up and improve accuracy. The system makes use of artificial intelligence algorithms to match surveillance camera realtime video footage with facial images of people who have gone missing. Face recognition technology in video surveillance systems is used in this project to come up with a strategy for locating people who have gone missing. The system involves gathering information about the missing person, creating a database of facial images, and matching those images to real-time video footage with the help of artificial intelligence algorithms. The goal of the field of computer science known as artificial intelligence (AI) is to create intelligent machines that can carry out tasks that typically call for human intelligence. This includes tasks like translating languages, visual perception, speech recognition, and decision-making. Machine learning algorithms are used by AI systems to learn from data and allow them to improve their performance over time. A powerful method for training artificial neural networks with many layers, deep learning, a subset of machine learning, has emerged, allowing. The system can be used to quickly identify and locate missing people in public places like airports and train stations. The speed and accuracy of missing person searches could be significantly enhanced by the proposed system, increasing the likelihood of successful reunions. Finding missing people in view of face acknowledgment utilizing Convolutional Brain Organization (CNN) calculation is a well-known approach that has shown promising outcomes. CNN is a deep learning algorithm that works well for face recognition because it is widely used for image recognition and classification.