In this paper, we introduce a novel discriminative feature which is efficient for pose estimation. The multi-view face representation is based on Local Gabor Binary Patterns( LGBP) and encodes the local facial characteristics in to a compact feature histogram. In LGBP, Gabor filters can extract the feature of the orientation of head and Local Binary Pattern(LBP) can extract the features of facial local orientation. To keep the spatial information of the multi-view face images, LGBP is operated on many subregions of the images. The combination of them can represent well and truly the multi-view face images. Considering the derived feature space, a radial basis function(RBF) kernel SVM classifier is trained to estimate pose. Extensive experiments demonstrate that the facial representation can be effective for pose estimation. is a face. The experimental results show that the proposed method is promising for the detection of occluded faces.