The human face is the subject of many studies in the field of artificial vision because of the high amount of semantic information. The most common of the studies carried out in this area are face analysis and expression. Automatic face recognition is used in many applications such as human-computer interaction, behavior analysis and marketing. In this study, it is aimed to use appearance based features obtained from the landmarks for instant facial expression recognition. In the study, the local binary pattern (LBP) attributes obtained from the surrounding of the landmarks using active shape models are used. In order to find the most discriminating subset of the obtained attributes, the selection of the attributes has been applied for improve the recognition rate. It has been shown that the method proposed in experiments with 10-fold cross-validation with the Cohn-Kanade dataset (CK+) which is containing seven different expression classes achieves %89.71 success rate.