Human emotions play an important role in decision making. Facial expression is natural and one of the most powerful immediate means for human beings to communicate their emotions and intentions. The face can express emotion sooner than people verbalize their feelings.As technology is advancing, the demand of face emotions recognition is increasing day by day for decision making. In past years, many researchers had introduced different techniques and different algorithms for accurate and reliable face emotion recognition. One key step in facial expression recognition is to extract the low dimensional discriminative features before the feature data are fed into classifier for emotions. In this paper, the authors have presented a new method of facial expression recognition based on statistical parameters of Qmatrix. The problem addressed here is to determine which features optimize classification rate so that, such features may be used in the evaluation of statistical features for face emotion recognition. The Q-matrix is much better than that of the remaining matrix methods, because it considers all possible neighbors of elements at once. The experimental results also demonstrate significant performance improvements due to the consideration of facial movement features and promising performance under face registration errors.