Optical flow-based algorithm analyses to detect human emotion from eye movement-image data

One of the popular methods for the recognition of human emotions such as happiness, sadness, and surprise is based on the deformation of facial features. Motion vectors that show these deformations can be specified by the optical flow. For this method, for detecting emotions, the resulted set of motion vectors is compared with a standard deformation template caused by human emotional changes. In this paper, a new method is introduced to compute the quantity of likeness to make decisions based on the importance of obtained vectors from an optical flow approach. The current study uses a feature point tracking technique separately applied to the five facial image regions (eyebrows, eyes, and mouth) to capture basic emotions. Moreover, this research will be focusing on eye movement regions. For finding the vectors, one of the efficient optical flow methods is using the pre-experiment as explained further below.