Automatic analysis of human facial expression is one of the challenging problems in machine vision systems. It has many applications in human-computer interactions, social robots, deceit detection, interactive video and behavior monitoring. In this paper, we developed a new method for automatic facial expression recognition based on verifying movable facial elements and tracking nodes in sequential frames. The algorithm plots a face model graph in each frame and extracts features by measuring the ratio of the facial graph sides. Seven facial expressions, including neutral pose are being classified in this study using support vector machine and other classifiers on JAFFE databases. The approach does not rely on action units, and therefore eliminates errors which are otherwise propagated to the final result due to incorrect initial identification of action units. Experimental results show that analyzing facial movements gives accurate and efficient information in order to identify different facial expressions.