Semi-supervised Facial Expression Recognition Algorithm on The Condition of Multi-pose

A major challenge in pattern recognition is labeling of large numbers of samples. This problem has been solved by extending supervised learning to semi-supervised learning. Thus semi-supervised learning has become one of the most important methods on the research of facial expression recognition. Frontal and un-occluded face images have been well recognized using traditional facial expression recognition based on semisupervised learning. However, pose-variants caused by body movement, may decrease facial expression recognition rate. A novel facial expression recognition algorithm based on semi-supervised learning is proposed to improve the robustness in multi-pose facial expression recognition. In the proposed method, transfer learning has been brought into semi-supervised learning to solve the problem of multi-pose facial expression recognition. Experiments show that our method is competent for semi-supervised facial expression recognition on the condition of multi-pose. The recognition rates are 82.68% and 87.71% on the RaFD database and BHU database, respectively.