Real Time Facial Expression Recognition With DeepLearning

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Real Time Facial Expression Recognition With DeepLearning

A real-time facial expression recognition system with webcam streaming and CNN

Overview:

The Real-Time Facial Expression Recognition system utilizes Convolutional Neural Networks (CNN) implemented by Keras to detect facial expressions through webcam streaming. The system captures the user's face in real-time using OpenCV, processes the images, and combines the spoken content with detected facial expressions to generate corresponding sentences with emoticons.

Features:

  • Real-Time Recognition: Provides real-time facial expression recognition through webcam streaming.
  • CNN Implementation: Uses CNN model implemented by Keras for facial expression detection.
  • Face Cropping and Resizing: OpenCV crops and resizes the detected face to 48x48 grayscale images for input to the deep learning model.
  • Combination with Spoken Content: Generates sentences with appropriate emoticons by combining spoken content with detected facial expressions.

Installation:

  1. Install Anaconda for package management and virtual environments.
  2. Create a virtual environment with Python 3.4 using Anaconda.
  3. Activate the virtual environment based on your operating system.
  4. Install dependencies in the following order:
    • Install scikit-learn.
    • Install OpenCV (version 3.1.0 recommended, update if needed).
    • Install Keras for high-level manipulation of deep learning models.
    • Install pandas for data preprocessing and h5py for saving model weights.
  5. Configure Keras to use Theano backend by modifying the configuration file.
  6. Follow the provided instructions to configure and use the system for facial expression detection.

Summary:

The Real-Time Facial Expression Recognition system combines CNN implemented by Keras with OpenCV for real-time facial expression detection through webcam streaming. By following the installation guide and configuring the system, users can detect and generate sentences based on spoken content and detected facial expressions.