Real Time Facial Expression Recognition With DeepLearning

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.