Detecting emotion from a person’s facial expression and analyzing it is crucial because of its applications including, but not limited to, lie detectors, human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a hot topic in computer vision, a lot of research has been conducted to develop a facial expression recognition (FER) system. These systems enable us to classify six basic emotions from image data: anger, disgust, fear, happiness, sadness and surprise.

The problem of detecting facial expressions seems like it can be easily solved by using convolutional neural networks. Convolutional neural networks have been used in facial expression recognition research for awhile, but there is inconsistency and fluctuation in recognition rate among classes. Most research done in this area has a lower recognition rate for detecting emotions like disgust and fear due to limited and imbalanced datasets. One possible solution is to use data augmentation for generating synthesized images to supplement training set in image classification.