社会与工程心理学研究室知识库

With the development of pattern recognition and artificial intelligence, emotion recognition based on facial expression has attracted a great deal of research interest. Facial emotion recognition are mainly based on facial images. The commonly used datasets are created artificially, with obvious facial expression on each facial images. Actually, emotion is a complicated and dynamic process. If a person is happy, probably he/she may not keep obvious happy facial expression all the time. Practically, it is important to recognize emotion correctly even if the facial expression is not clear. In this paper, we propose a new method of emotion recognition, i.e., to identify three kinds of emotion: sad, happy and neutral. We acquire 1347 3D facial points by Kinect V2.0. Key facial points are selected and feature extraction is conducted. Principal Component Analysis (PCA) is employed for feature dimensionality reduction. Several classical classifiers are used to construct emotion recognition models. The best performance of classification on all, male and female data are 70%, 77% and 80% respectively.