Review on learning framework for facial expression recognition

The facial expressions recognition (FER) is crucial to many applications. As technology advances and our needs evolve, compound emotion recognition is becoming increasingly important, along with basic emotion recognition. In the literature, Although, FER can be conducted primarily using multiple sensors. However, research shows that using facial images/videos to recognize facial expressions is better because visual presentation can convey more efficiently. Among state-of-the-art methods for FER systems, to improve the accuracy of the basic and compound FER systems, detection of facial action units (AUs) must be combined to detect basic and compound facial expressions. State-of-the-art results show that machine learning and deep learning-based approaches are more potent than conventional FER approaches. This paper surveys various learning frameworks for facial emotion recognition systems for detecting basic and compound emotions using the diverse database and summarizing state-of-the-art results to give good understanding of impact of each learning framework used in FER systems.