Emotion recognition is a challenging problem in the field of computer vision. The automatic classification of emotions using facial expressions is a promising approach to understand human behavior in various applications such as marketing, health, and education. How- ever, recognizing some emotions, such as anger, jealousy, contempt, and disgust, is more challenging than others due to their subtlety and rarity in the training data. In this paper, we try to investigate if using (self)pseudo-labelled data to train an Expression Manipulator [? ] generator to generate a training set for training a classifier is a better alternative to directly using an equal amount of (self)pseudo-labelled data for training the classifier [? ]. Specifically, we focus on augmenting the Action Units (AUs) of facial expressions, which are the basic units of facial movement that correspond to specific emotions