Micro-expression is a fast leaked facial expression which is characterized by its short duration and low intensity. It can be effectively applied in lie detection as well as many other fields of studies. The project employs computer vision techniques and research methods from cognitive psychology to develop micro-expression automatic recognition algorithms and models. Employing point distribution model to automatically detect micro-expression; constructing a database for the micro-expression recognition. Analyzing the color space and utilize color information to further increase the accuracy of the micro-expression recognition. To address the characteristic of micro-expression, we investigate the sparse representation of micro-expressions and represent micro-expressions as tensors to preserve its temporal spatial information. The project proposes to perform a different transformation algorithm for each mode of the tensor according to the real meaning of the data in each mode (i.e. a mode may represent a facial image, color space, or temporal data). After the project is completed, through acquiring theoretical and critical technological breakthrough in the field of automated micro-expression recognition; increasing the efficiency and accuracy of automated micro-expression recognition.