关键词: 主动外观模型, 尺度不变特征变换, 梯度方向直方图, 特征分组, 多姿态

Abstract: By means of locating key points by Active Appearance Model（AAM） and describing each key point by Scale-Invariant Feature Transform（SIFT）, a new method for facial expression feature extraction is proposed. The gradient direction histogram around these points is used to describe features of expression. Meanwhile, this paper puts the feature points into group and gives them different weights according to their contributions to the expression recognition, and uses the two-level Support Vector Machine（SVM） to classify the category of the fused and weighted features. The experimental results of standard expression databases and multi-pose expression database show that the proposed method not only improves the recognition rates of the frontal face, but also has better robustness for the expression of the non frontal face under some deflection angles.

Key words: active appearance model, Scale Invariant Feature Transform（SIFT）, gradient direction histogram, feature grouping, multi-pose