This paper proposes a new pose estimation method based on the appearance of 2D head image. First, the 1D Gabor filters are used to extract the features on the raw images. Compared with the traditional 2D Gaborrepresents, the 1D Gabor represents are more closely related to the head pose, while the advantages of computation and storage are obvious. Second, for the extracted features, a new method, named kernel local fisher discriminant analysis, is applied to eliminate the multimodal problem, while at the same time enhance the discrimination ability.Experimental results show that the proposed method is effective for pose estimation. It must be pointed out that the generalizability of the proposed method is illustrated by the impressive performance when the training dataset and the testing dataset are heterogeneous.