  Facial expression recognition (FER) has always been a challenging issue in
computer vision. The different expressions of emotion and uncontrolled
environmental factors lead to inconsistencies in the complexity of FER and
variability of between expression categories, which is often overlooked in most
facial expression recognition systems. In order to solve this problem
effectively, we presented a simple and efficient CNN model to extract facial
features, and proposed a complexity perception classification (CPC) algorithm
for FER. The CPC algorithm divided the dataset into an easy classification
sample subspace and a complex classification sample subspace by evaluating the
complexity of facial features that are suitable for classification. The
experimental results of our proposed algorithm on Fer2013 and CK-plus datasets
demonstrated the algorithm's effectiveness and superiority over other
state-of-the-art approaches.
