Human Emotion Recognition using Convolutional Neural Network in Real Time

The human emotion recognition has attracted interest of many problem solvers in the field of artificial intelligence. The emotions on a human face say so much about our thought process and give a glimpse of what's going on inside the mind. Real time emotion recognition is to acquaint the machine with human like ability to recognize and analyse human emotions. This project aims to categorize a facial image into one of the seven emotions which we are considering in this study, by building a multi class classifier. In this paper we are using convolutional neural networks (CNNs) for training over gray scale images obtained from fer2013 dataset. We experimented with different depths and max pooling layers to get the best accuracy and ultimately achieving 89.98% accuracy. To combat overfitting, we have used technique like dropout. We are also analyzing the performance of different network architectures like shallow network and modern deep network in recognizing human emotion. We also present the real-time implementation of emotion recognition in web-camera which provides accurate results for multiple faces simultaneously. The results obtained from the research are quite interesting.