Integrate Face Emotion Recognition Using Machine Learning

[shivenyadavs]

Add a new feature to the project that uses face emotion recognition through video input. This feature will leverage Machine Learning, Convolutional Neural Networks (CNN), and frameworks such as TensorFlow and Keras to detect and classify human emotions in real-time.

Key Tasks:
Set Up Environment:

Install the required libraries (e.g., TensorFlow, Keras, OpenCV, etc.).
Configure the project to handle video input via webcam.
Data Preprocessing:

Use a facial detection library (e.g., OpenCV's Haar cascades or Dlib) to isolate faces in video frames.
Normalize and preprocess the input for emotion recognition.
Model Integration:

Use a pretrained emotion detection model (or train one using a dataset like FER-2013 or CK+).
Implement the model using TensorFlow/Keras for emotion prediction.
Real-time Emotion Recognition:

Process live video frames to detect faces and classify emotions.
Overlay bounding boxes and emotion labels on the video feed.
UI/UX Integration:

Display the real-time video feed with emotion recognition results.
Integrate this feature seamlessly with the existing game or as a standalone functionality.
Optional Enhancements:

Add a feature to log or save the detected emotions.
Implement multi-face emotion detection in the video feed.