Facial Emotion Recognition

Facial Emotion Recognition plays a significant role in interacting with computers which help us in various fields like medical processes, to present content on the basis of human mood, security and other fields. It is challenging because of heterogeneity in human faces, lighting, orientation, poses and noises. This paper aims to improve the accuracy of facial expression recognition. There has been much research done on the fer2013 dataset using CNN (Convolution Neural Network) and their results are quite impressive. In this work we performed CNN on the fer2013 dataset by adding images to improve the accuracy. To our best knowledge, our model achieves the accuracy of 70.23 % on fer2013 dataset after adding images in training and testing parts of disgusted class.