Facial-Expression-Recognition

In this project, you will build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces. The objective is to classify each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). You will use OpenCV to automatically detect faces in images and draw bounding boxes around them. Once you have trained, saved, and exported the CNN, you will directly serve the trained model predictions to a web interface and perform real-time facial expression recognition on video and image data.

Key Concepts

1.Develop a facial expression recognition model in Keras

2.Build and train a convolutional neural network (CNN)

3.Deploy the trained model to a web interface with Flask

4.Apply the model to real-time video streams and image data