Hardware Limitations: Implementing face recognition with limited hardware resources.
Cluster Selection: Choosing the right cluster for optimal face recognition performance.
Accuracy and Speed: Ensuring fast and accurate recognition for precise time tracking.
Performance and Scalability: Maintaining responsiveness and stability under high loads.
Multiple Face Detection: Supporting simultaneous recognition of multiple faces.
Application Usage Monitoring: Accurately tracking time spent on different applications.
We adopted a modular approach, dividing the project into sub-projects for efficient development: back-end, front-end, and desktop listener. The team has created a platform compatible with both desktop PCs (using standard USB cameras) and resource-constrained Linux platforms (like Raspberry Pi). The system operates asynchronously, processing captured images in the background to maintain optimal performance.
I enjoyed working on new product development because you can see the tangible results of your work and even hold the device in your hands. Developing face recognition technology for this device was a complex but rewarding challenge.
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