Robust real-time emotion detection system using CNN architecture

As the human–robot interaction is catching eye day by day with the increase in need of automation in every field, personal robots are increasing in every area which may be coping needs of elderly people, treating autistic patients or child therapy, even in the area of babysitting the child. As robots are helping human being in all such cases, robots need to understand human emotion in order to treat human in a more customized manner. Predicting human emotion has been a difficult problem which is being solved over a decade’s time. In this paper, we have built a model which can predict human emotion from an image in real time. The network build is based on convolutional neural network which has reduced parameters by 90× from that of Vanilla CNN and also 50× from the latest state-of-the-art research carried out to the best of our knowledge. The network build is tested robustly on 8 different datasets, namely Fer2013, CK and CK+, Chicago Face Database, JAFFE Dataset, FEI face dataset, IMFDB, TFEID and custom dataset build in our laboratory having different angles, faces, backgrounds and age groups. The network achieves 74% accuracy which is an improved accuracy from the state-of-the-art accuracy with reduced computation complexity.