SEARCHING MISSING PEOPLE BASED ON FACE RECOGNITION USING AI IN VIDEO SURVELLIANCE SYSTEM
DOI:
https://doi.org/10.29121/shodhkosh.v5.i3.2024.4705Keywords:
Artificial Intelligence, Convolutional Neural Network, Deep Learning, Face Detection, Image RecognitionAbstract [English]
Finding missing persons based on face recognition using AI is a promising approach that can significantly improve the speed and accuracy of missing person searches. The system involves using AI algorithms to match facial images of missing persons with real-time video footage from surveillance cameras. This paper proposes a method for finding missing persons using face recognition technology in video surveillance systems. The system involves collecting data about the missing person, building a database of facial images, and using AI algorithms to match those images with real-time video footage. Artificial intelligence (AI) is a field of computer science that aims to develop intelligent machines that can perform tasks that typically require human intelligence. This includes tasks such as visual perception, speech recognition, decision-making, and language translation. AI systems are designed to learn from data, using machine learning algorithms that allow them to improve their performance over time. Deep learning, a subset of machine learning, has emerged as a powerful technique for training artificial neural networks with many layers, enabling AI systems to recognize complex patterns and make accurate predictions. The system can be implemented in public spaces, such as airports and train stations, to quickly identify and locate missing persons. The proposed system has the potential to significantly improve the speed and accuracy of missing person searches, thereby increasing the likelihood of successful reunions. Finding missing persons based on face recognition using Convolutional Neural Network (CNN) algorithm is a popular approach that has shown promising results. CNN is a deep learning algorithm that is widely used for image recognition and classification tasks, making it suitable for face recognition.
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Copyright (c) 2024 S. Sowmiya, S. Akash, M. Sanjay Kumar, Sankar Raja, S. Arun
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