Facial Expression Recognition using Convolutional Neural Network

Facial expression is a way of non-verbal communication by using eyes, lips, nose and facial muscles. Smiling and rolling eyes are some examples. Facial expression recognition is the process of extracting facial features from a person. Facial expressions include anger, happy, disgust, sad, neutral, fear and surprise. By the use of machine learning, an expression recognition model is built using Convolutional Neural Network. The input data is fed to the system in order to give the expected results. The model is trained using Facial Expression Recognition (FER) dataset. The Convolutional Neural Network (CNN) gives good and accurate results. The Haar cascade classifier classifies the face and non-face regions in the input image which helps the convolutional network to classify the images. Good classification of images can be desirable by the use of classifiers. These classifiers can be implemented by using the OpenCV library.