In this paper, a robust pre-processing algorithm based on spatial fuzzy clustering for a model based lip contour extraction is presented. This transformation aims at increasing discrimination between lips and skin to enhance the lip contour detection. For each pixel in the transformed data, a and b components of CIELAB color space are used as features in spatial fuzzy clustering phase. The employed clustering method takes into account both the distributions of data in feature space and the spatial interactions between neighboring pixels during clustering. Then, an elliptical and a Gaussian mask delete the pixels around lip that were previously clustered as lip pixels. Finally, a model is fitted upon the lip for extracting the lip contour. To show the effectiveness of the proposed method, pseudo hue segmentation and spatial fuzzy c-mean clustering were implemented and compared to our method. The results on VidTIMIT and M2VTS databases show that the novel scheme is able to extract the lip contour even in the situations that the nose and neck appeared in the lip region. Empirical results showed our method outperformed the present state-of-art methods.