• Face Detection using dlib:

  • Employ the 'Human Face Detector' model from the dlib library to identify and locate faces within the video feed.
  • Utilize the face coordinates obtained to crop the facial region for further analysis.

• Data Preprocessing:

  • Normalize and preprocess the cropped facial images to ensure consistency and enhance the model's interpretability.
  • Apply any necessary data augmentation techniques to improve model generalization.

• Fine-tuned CNN Model:

  • Develop a Convolutional Neural Network (CNN) based on the ResNet50 architecture.
  • Fine-tune the model on a relevant dummy dataset, incorporating a diverse range of facial expressions to enhance its ability to recognize various engagement states.

• Engagement Prediction:

  • Train the model to classify the extracted features to detect whether the student is engaged or not.
  • Validate the model's performance using a dummy evaluation dataset to ensure its accuracy and generalization capabilities.

• Real-time Video Analysis:

  • Implement a real-time video analysis module that continuously captures frames from the video feed.
  • Apply the trained model to the cropped facial regions to predict student engagement at regular intervals during online classes.

• Engagement Report Generation:

  • Accumulate the predictions over time to generate statistics on student engagement.
  • Create visualizations, such as graphs or charts, to present the data in an easily interpretable format.
  • Include metrics such as average engagement, engagement fluctuations, and duration of sustained attention.

• Application Development:

  • Develop a user-friendly application that integrates the entire student engagement detection system.
  • Implement features for configuring analysis intervals, visualizing real-time engagement data, and generating comprehensive reports.

• Deployment and Integration:

  • Deploy the application to be seamlessly integrated into online class platforms.
  • Ensure compatibility with various video conferencing tools and adaptability to different hardware configurations.

• Continuous Improvement:

  • Regularly update the model with new data to adapt to evolving student engagement patterns.
  • Gather feedback from educators and users to identify areas for improvement and implement necessary enhancements in subsequent versions of the system.

HuggingFace Spaces Demo Link