Employee Monitoring System using Face Recognition Techniques

This research paper introduces an innovative Employee Monitoring System that utilizes Face Recognition Techniques, addressing the changing landscape of remote work, particularly heightened by the global COVID-19 pandemic. Employing deep learning, specifically focusing on the ResNet50 architecture, and real-time image analysis, the system aims to redefine how organizations monitor and empower their remote workforce. The introduction establishes the transformative potential of the project, positioning it as a pioneering effort in the dynamic realm of modern work. Delving into the scope, the project surpasses conventional monitoring systems by incorporating Haar Cascade face detection technology, offering real-time insights into individual activities during remote work. The paper meticulously details technical intricacies, including data collection methodologies, preprocessing techniques, and the development of a user-friendly web application. This comprehensive approach vividly illustrates the system's capabilities and its potential to optimize various facets of remote work operations. The paper's organization ensures a logical progression, commencing with the conceptualization and context in the introduction, followed by an in-depth exploration of technical aspects within the scope. Gratitude expressed to the project's contributors underscores the collective effort, from the invaluable guidance of the academic advisor to the collaborative endeavors of the exceptional team. The project's success stands as a testament to the collaborative spirit, innovative thinking, and dedication of all involved, with anticipated positive impacts on enhancing employee monitoring processes in the evolving landscape of remote work.