This paper describes a new method for pose estimation of human face moving abruptly in real world. The virtue of this method is to use a very simple calculation, disparity, among multiple model images, and not to use any facial features such as facial organs. In fact, since the disparity between input image and a model image increases monotonously in accordance with the change of facial pose, view direction, we can estimate pose of face in input image by calculating disparity among various model images of face. To overcome a weakness coming from the change of facial patterns due to facial individuality or expression, the first model image of face is detected by employing a qualitative feature model of frontal face. It contains statistical information about brightness, which are observed from a lot of facial images, and is used in model-based approach. These features are examined in everywhere of input image to calculate faceness" of the region, and a region which indicates the highest faceness" is taken as the initial model image of face. To obtain new model images for another pose of the face, some temporary model images are synthesized through texture mapping technique using a previous model image and a 3-D graphic model of face. When the pose is changed, the most appropriate region for a new model image is searched by calculating disparity using temporary model images. In this serial processes, the obtained model images are used not only as templates for tracking face in following image sequence, but also texture images for synthesizing new temporary model images. The acquired model images are accumulated in memory space and its permissible extent for rotation or scale change is evaluated. In the later of the paper, we show some experimental results about the robustness of the qualitative facial model used to detect frontal face and the pose estimation algorithm tested on a long sequence of real images including moving human face.