In order to improve the real-time performance of the Gradient Vector Flow Snake(GVF Snake) algorithm for face contour tracking in a dynamic image sequence and to overcome the occlusion problem in face tracking, a novel image extraction method combining the GVF Snake algorithm and the single variable first-order grey model GM(1,1) is proposed to extract the face contour. In this method, the moving face contour is roughly detected out firstly by using human motion information and the skin-color model, and then the accurate face contour is extracted by using the GVF Snake algorithm, by which the initialization problem of the GVF Snake algorithm is solued. For the integrity feature of face contour motion, the GM(1,1) model is used to predict the centroid position of face contour and then the position is used as the iteration basis of the GVF Snake algorithm. Meanwhile, the centroid position of face contour extracted with GVF Snake is taken as the prediction basis of the GM(1,1) model for the next frame. When the occlusion exists, the continuity of tracking can be held with the prediction of GM(1,1) model. Experimental results show that by proposed method, the average tracking time and the average tracking error are only 8.0% and 312% of those of the GVF Snake algorithm respectively. It can be concluded that this method can better reflect the motion law of face contour, and has strong real-time performance and good robustness.