Facial Expression Recognition using Convolutional Neural Network and Haar Classifier

Human emotion recognition is essential for human-machine interaction and interpersonal communication. Utilizing facial expression analysis, this proposed work investigates Facial Expression Recognition (FERS) method using convolutional neural network and HAAR classifier. Face detection begins with the HAAR cascade model, lighting alteration to achieve face homogeneity, and morphological approaches to keep the key face component. The use of our cutting-edge deep convolutional network provides the gadget with capabilities comparable to those of a person. We have a tendency to appropriately project both a superficial and deep network for typical human face expressions. We have also adjusted several of the network's parameters and filters to boost accuracy 98.4%. Our proposed model is able to accurately categorise seven distinct emotional states.