In situations in which individuals are motivated to conceal or repress their true emotions, their facial expressions may leak despite their efforts to conceal them. These leakages can be very useful for deception detection and many of these leakages are manifested in the form of micro-expressions. However, it is difficult for human to detect micro-expressions. Due to this incompetence, researchers have to manually inspect large amount of videos in frame by frame manner. It has become the greatest impediment for micro-expression studies. The combination of computer science and psychology research fields can provide the technical adjuncts to assist researchers and practitioners in micro-expression analysis. In this study, a novel approach for automatic micro-expression recognition is presented. By extracting the features of textures and shapes from static images, the final system are able to automatically spot and recognize the micro-expressions. This system will prove to be a very useful tool for the researchers who are interested in investigating the generation of micro-expressions. To build a system that is robust and applicable in clinical practice, research activity, national security, and criminal investigations, the system mentioned above must to be capable of extracting the facial dynamics from facial expressions. However, at present stage, it is difficult for the researchers to utilize the dynamic features of micro-expressions because of the appropriate psychological base for this method is still missing. To solve this problem, the effects of dynamic information on micro-expression recognition were investigated in this study. Results showed that, subjects were unable to utilize the dynamic information of micro-expressions to recognize intense micro-expressions even after receiving the METT training program. However, when the intensity of micro-expressions was low, the recognition accuracy of the subjects was promoted by dynamically presenting the micro-expressions. Based on the investigations mentioned above, this study proposed a preliminary system framework for the dynamic automatic micro-expression system. This study provides the algorithmic base and psychological base for building an automatic micro-expression recognition system which is robust across all situations.