Real-Time Face Recognition System for Remote Employee Tracking
This addresses a specific challenge for companies needing to monitor employee productivity in remote work settings, but it is incremental as it applies existing methods to a new application.
The paper tackles the problem of tracking remote employees during the COVID-19 pandemic by developing a real-time face recognition system, achieving 97.8% accuracy using FaceNet with KNN on the LFW dataset.
During the COVID-19 pandemic, most of the human-to-human interactions have been stopped. To mitigate the spread of deadly coronavirus, many offices took the initiative so that the employees can work from home. But, tracking the employees and finding out if they are really performing what they were supposed to turn out to be a serious challenge for all the companies and organizations who are facilitating "Work From Home". To deal with the challenge effectively, we came up with a solution to track the employees with face recognition. We have been testing this system experimentally for our office. To train the face recognition module, we used FaceNet with KNN using the Labeled Faces in the Wild (LFW) dataset and achieved 97.8\% accuracy. We integrated the trained model into our central system, where the employees log their time. In this paper, we discuss in brief the system we have been experimenting with and the pros and cons of the system.