Classification of Facial Expression for Emotion Recognition using Convolutional Neural Network

Automatic facial expression classification is very demanding research field because of its application in the field of health, safety and human machine interfaces. Many attempts by the researchers have been made in developing methodologies which can interpret, decode facial expression and obtain important features from the facial images to achieve better classification result. With the advancement in the data capturing techniques and various deep learning architectures it is possible to achieve higher accuracy in the computer vision task like facial expression classification. The aim of this research paper is to propose Custom-CNN architecture for the facial expression classification and performance of the model is compared with other standard pre-trained deep convolutional neural networks. Kaggle dataset comprises 35,900 is utilized to train, validate and test CNN models.