Analysis of CPU Utilization of a Cross-Platform Web Application for Facial Recognition based Remote User Tracking System

One of the most common and integral tasks in any institution is to track and maintain attendance records of its employees. Whilst there are multiple options available at present to accomplish this, they are either time consuming or often prone to errors. More importantly, they are incompatible with different kinds of mobile handsets that employees use nowadays. To tackle this challenge, we propose a simple user-friendly cross-platform mobile application using Flutter – a cross-platform application development framework that accommodates facial recognition and GPS tracking features. Besides these aspects, the CPU utilization of the proposed mobile application is also analyzed, ensuring it uses minimal computational resources. The proposed application is able to track a user’s location and record attendance precisely and remotely, minimizing the need for manual logging of attendance. This provision can play an integral role in these post-pandemic times, as it eliminates the requirement for users to use fingerprint-based or RFID-based attendance systems. Additionally, the GPS features make it feasible for employees working remotely from their homes, or on location such as a client visit, to clock-in their attendance. The proposed application can help reduce paperwork required in traditional attendance monitoring as well human errors, while also eliminating long queues prevalent in traditional attendance systems and other health and safety risks – making it a powerful real-world tool following the Covid-19 pandemic.