Addressing Class Imbalance in Facial Emotion Recognition

Abstract

The wide usage of computer vision in many perspectives has been attracted in the recent years. One of the areas of computer vision that has been studied is facial emotion recognition, which plays a crucial role in the interpersonal communication. This work demonstrates the advances could be made in this eld. This work tackles the problem of intraclass variances in the face images of emotion recognition datasets. We test the system on an augmented dataset including CK+, EMOTIC, and KDEF dataset samples, which increase the intraclass variances in the face images of our dataset. The proposed method is based on SMOTETomek.