Abstract
In contemporary office environments, effective resource management and access control are essential for maintaining security and optimizing productivity. Traditional methods of resource management and access control, such as manual logging and key- based systems, are often cumbersome, prone to errors, and lack real-time monitoring capabilities. In this paper, we propose a Face Recognition-Based Office Resource Controlling (FROC) system that leverages the capabilities of facial recognition technology to automate resource management and access control processes. The FROC system utilizes advanced deep learning algorithms to accurately identify individuals based on facial biometrics, granting or restricting access to office resources accordingly. By integrating facial recognition technology with centralized resource management software, the FROC system enables seamless and efficient control over office facilities, equipment, and confidential information. Additionally, the system offers real-time monitoring and logging of resource usage, enhancing security and accountability within the office environment. Through a combination of hardware components, including surveillance cameras and access control devices, and software modules for facial recognition and resource tracking, the FROC system provides a comprehensive solution for modern office resource management. We evaluate the performance and effectiveness of the FROC system through extensive testing in simulated office environments, demonstrating its potential to streamline operations, improve security, and optimize resource utilizationin office settings. Keywords: Convolutional Neural Networks, image recognition, Tensor Flow, Deep Learning, Teachable Machine,
Published Version
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