Facial Expression Recognition Based on Complexity Perception Classification Algorithm
This work addresses variability in facial expression recognition for computer vision applications, but it is incremental as it builds on existing CNN models with a new classification approach.
The paper tackled the challenge of inconsistent complexity in facial expression recognition by proposing a complexity perception classification algorithm that divides datasets into easy and complex subspaces, achieving superior results on Fer2013 and CK-plus datasets compared to state-of-the-art methods.
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.