Challenges

  • 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.

Our Approach

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.

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