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In this paper, a novel method based on pose adaptive linear discriminant analysis (PALDA) is proposed to deal with pose variation problems in face recognition when each person has only one frontal training sample. The basic idea of the PALDA method is described as follows: first, the pose style of the test sample is estimated by one pose classifier; then, the corresponding LDA feature, which is robust to the variation between the estimated pose style and the frontal pose style, is extracted. Since human faces are very similar objects with similar geometrical shape and configuration, the facial images from the same pose style are similar to each other. So an effective pose classifier is designed. The variations of each specific personpsilas facial images, due to changes of pose, are also rather similar to each other, so varieties of LDA projection matrixes, each of which is robust to one variation between one specific pose style and the frontal pose style, are trained with offline face samples containing various poses. The comparison with other pose robust face recognition methods on CMUPie database has confirmed the effectiveness and the robustness of the proposed method.