The most expressive way human beings display emotions is through facial expressions. The task of detecting facial
expression of a human being via a computer is a very complex process due to its variability present across human faces
including color, expression, position, and orientation. The aim of this paper is to presents a Convolution Neural Network (CNN)
architecture for real-time facial expression recognition. We have used ICML 2013 Facial Expression Recognition Challenge
dataset for this study and then trained our neural network for emotion state classification. In this study, we achieved accuracy of
84.18% and validation accuracy of 67.56% for classification of seven different emotions through facial expressions.
