Abstract: Feature extraction is a basis, a vital step and a major issue in facial expression recognition. To
ensure that the extracted features can be more comprehensive characterization of a certain kind of expression,
we present a feature extraction method based on fused geometry and local texture features. Geometric features
are obtained from the feature points marked by AAM (Active Appearance Model) algorithm, texture feature
extraction is based on LBP (Local Binary Pattern) algorithm, the dimension of fusion expression features is
reduced by LLE ( Locally Linear Embedding) algorithm. Finally, a multi-class SVM ( Support Vector
Machine) is used for facial expression classification. Our method is deployed on the JAFFE and Yale data
sets, the results show a recognition accuracy of 98. 57% and 91. 67% respectively, which prove the
effectiveness of our proposed method.

Key words: local binary pattern(LBP), support vector machine (SVM).,  facial expression recognition, active appearance model (AAM), locally linear embedding (LLE)