Biblio

Teaching evaluation is an indispensable key link in the modern education model. Its purpose is to promote learners' cognitive and non-cognitive development, especially emotional development. However, today's education has increasingly neglected the emotional process of learners' learning. Therefore, a method of using machines to analyze the emotional changes of learners during learning has been proposed. At present, most of the existing emotion recognition algorithms use the extraction of two-dimensional facial features from images to perform emotion prediction. Through research, it is found that the recognition rate of 2D facial feature extraction is not optimal, so this paper proposes an effective the algorithm obtains a single two-dimensional image from the input end and constructs a three-dimensional face model from the output end, thereby using 3D facial information to estimate the continuous emotion of the dimensional space and applying this method to an online learning system. Experimental results show that the algorithm has strong robustness and recognition ability.

This paper explores the benefits of 3D face modeling for in-the-wild facial expression recognition (FER). Since there is limited in-the-wild 3D FER dataset, we first construct 3D facial data from available 2D dataset using recent advances in 3D face reconstruction. The 3D facial geometry representation is then extracted by deep learning technique. In addition, we also take advantage of manipulating the 3D face, such as using 2D projected images of 3D face as additional input for FER. These features are then fused with that of 2D FER typical network. By doing so, despite using common approaches, we achieve a competent recognition accuracy on Real-World Affective Faces (RAF) database and Static Facial Expressions in the Wild (SFEW 2.0) compared with the state-of-the-art reports. To the best of our knowledge, this is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.