This project aims to use facial recognition technology to monitor employee productivity and track specific activities in the workplace.
You can visit the SSL encrypted website hosted on AWS https://www.cwem.site/
- Detects whether the right person is sitting in front of the camera
- Tracks key points on the face (such as eyes, mouth) to identify specific activities
- Classifies the type of activity the user is performing (e.g. taking a phone call, looking away from the screen, sleeping, looking tired)
- Lightweight and runs in the browser
- Sends summary data about activities to a central server in JSON format, without transmitting any images or videos
- Automatic Database Records: The system automatically adds records of employee activities to the database, eliminating the need for manual data entry. This ensures accurate and up-to-date tracking of employee actions and enables comprehensive reporting and analysis of productivity metrics.
- A computer with a webcam
- A modern web browser (such as Chrome or Firefox)
- Clone this repository to your local machine
- Install the necessary dependencies by running
pip install -r requirements.txt - Run the app by executing
python app.py - Open your web browser and navigate to
http://localhost:8080
- Open the project in GitHub Codespaces.
- Update the apt package manager and install FFmpeg:
- Install the necessary dependencies by running
pip install -r requirements.txt - Run the application by executing
python app.py - Open your web browser and navigate to http://localhost:8080
sudo apt update
sudo apt-get install ffmpeg
- dlib or MTCNN for facial recognition and keypoint detection
- Machine learning or deep learning for activity classification
- Flask for the web server
We take the privacy of our employees seriously. No images or videos of users are transmitted to the central server - only summary data about their activities is sent. All data is handled in accordance with relevant privacy laws and regulations.