Unsupervised lips segmentation based on ROI optimisation and parametric model

Abstract : Lips segmentation is a very important step in many applications such as automatic speech reading, MPEG-4 compression, special effects, facial analysis and emotion recognition. In this paper, we present a robust method for unsupervised lips segmentation. First the color of the lips area is estimated using expectation maximization and a membership map of the lips is computed from the skin color distribution. The region of interest (ROI) is then found by automatic thresholding on the membership map. Given a mask of the ROI, we initialize a snake that is fitted on the upper and lower contour of the mouth by multi level gradient flow maximization. Finally to find the mouth corners and the final contour of the mouth, we use a parametric model composed of cubic curves and Bezier curves.
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Christian Bouvier, Pierre-Yves Coulon, Xavier Maldague. Unsupervised lips segmentation based on ROI optimisation and parametric model. 14th IEEE International Conference on Image Processing (ICIP 2007), Sep 2007, San Antonio, Texas, United States. pp.IV-301-304, ⟨10.1109/ICIP.2007.4380014⟩. ⟨hal-00372142⟩

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