Abstract
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, which lack the description of 3D spatial information of micro-expressions. Therefore, we proposed a new micro-expression database containing 2D video sequences and 3D point clouds sequences. The database includes 259 micro-expressions sequences, and these samples were classified using the objective method based on facial action coding system, as well as the non-objective method that combines video contents and participants' self-reports. We extracted facial 2D and 3D features using local binary patterns on three orthogonal planes and curvature descriptors, respectively, and performed baseline evaluations of the two features and their fusion results with leave-one-subject-out(LOSO) and 10-fold cross-validation methods. The best fusion performances were 58.84% and 73.03% for non-objective classification and 66.36% and 77.42% for objective classification, both of which have improved performance compared to using LBP-TOP features only.The database offers original and cropped micro-expression samples, which will facilitate the exploration and research on 3D Spatio-temporal features of micro-expressions.
Abstract (translated)
URL
https://arxiv.org/abs/2108.00166
