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.

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.

Hui Po Wang

Real Time Facial Expression Recognition With DeepLearning