Spontaneous Smile Recognition for Interest Detection

“Interest”is a critical bridge between cognitive and effective issues in learning. Student’s interest has great impact on learning performance. Hence, it’s necessary to detect student’s interest and make them more engaged in the learning process for productive learning. Student’s interest can be detected based on the facial expression recognition, e.g., smile recognition. However, various head poses, different illumination, occlusion and low image resolution make smile recognition difficult. In this paper, a conditional random forest based approach is proposed to recognize spontaneous smile in natural environment. First, image patches are extracted within the eye and mouth regions instead of the whole face to improve the robustness and efficiency. Then, the conditional random forests based approach is presented to learn the relations between image patches and the smile/non-smile features conditional to head poses. Furthermore, a K-means based voting method is introduced to improve the discrimination capability of the approach. Experiments have been carried out with different spontaneous facial expression databases. The encouraging results suggest a strong potential for interest detection in natural environment.