Real-Time Face Recognition System for Remote Employee Tracking

TL;DR

This paper presents a real-time face recognition system designed for remote employee tracking during the COVID-19 pandemic, utilizing FaceNet and KNN to achieve high accuracy in identifying employees working from home.

Contribution

The paper introduces a practical face recognition system for remote employee monitoring, including implementation details and experimental evaluation.

Findings

Achieved 97.8% accuracy with FaceNet and KNN on LFW dataset.

Successfully integrated the face recognition model into a remote work monitoring system.

Discussed pros and cons based on experimental testing.

Abstract

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.