Robust Lip Segmentation Based on Complexion Mixture Model

Lip image analysis plays a vital role in Traditional Chinese Medicine (TCM) and other visual and speech recognition applications. However, if the lip images contain weak color difference with background parts or the background is complicated, most of the current methods are difficult to robustly and accurately segment the lip regions. In this paper, we propose a lip segmentation method based on complexion mixture model to resolve this problem. Specifically, we use the pixels’ color of the upper (lip-free) part of the face as training data to build a corresponding complexion Gaussian Mixture Model (GMM) for each face image in Lab color space. Then by iteratively removing the complexion pixels not belonging to the lip region in the lower part of the face based on the GMM, an initial lip can be obtained. We further build GMMs on the initial lip and non-lip regions, respectively. The background probability map can be obtained based on the GMMs. Finally, we extract the optimal lip contour via a smooth operation. Experiments are performed on our dataset with 1000 face images. Experimental results demonstrate the efficacy of the proposed method compared with the state-of-art lip segmentation methods.