Facial expression recognition is an active problem of behavioral science since Darwin's work which was presented in 1872. Nowadays, development of imaging techniques and computer technology leads this problem to have various application areas like education of autistic children, non-verbal communication, human-machine interaction. In this study, we propose a novel feature extraction method to recognize seven facial expressions. After the detection of facial landmarks by using the supervised descent method, high level features are extracted from the variations of landmarks through the video frames. Recognition experiments have been performed on one of the most widely used datasets, namely CK+ database by using support vector machines in classification. Experimental results demonstrate that facial expressions have been successfully recognized.