A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation

—Micro-expressions are spontaneous, unconscious facial movements that show people's true inner emotions and have great potential in related fields of psychological testing. Since the face is a 3D deformation object, the occurrence of an expression can arouse spatial deformation of the face, but limited by the available databases are 2D videos, lacking the description of 3D spatial information of micro-expressions. In this paper, we proposed a new database that contains 373 micro-expression samples, each consisting of 2D video sequence and corresponding 3D point cloud sequence. These samples were classified using objective method based on facial action coding system, as well as non-objective emotion classification method combining video contents and participants’ self-reports. We extracted 2D and 3D features using the local binary patterns on three orthogonal planes (LBP-TOP) and curvature algorithms, respectively, and evaluated the classification accuracies of these two features and their fusion results with leave-one-subject-out (LOSO) and 10-fold cross-validation. Further, we used various advanced neural network algorithms for database evaluation, the results show that classification accuracies are improved by fusing 3D features than using only 2D features. The database offers original and cropped micro-expression samples, which will facilitate the exploration and research on 3D spatio-temporal features of micro-expression.