Parametric models for facial features segmentation

Abstract

-In this paper, we are dealing with the problem of facial features segmentation (mouth, eyes and eyebrows). A specific parametric model is defined for each deformable feature, each model being able to take into account all the possible deformations. In order to initialize each model, some characteristic points are extracted on each image to be processed (for example, eyes corners, mouth corners and brows corners). In order to fit the model with the contours to be extracted, a gradient flow (of luminance or chrominance) through the estimated contour is maximized because at each point of the searched contour, the gradient (of luminance or chrominance) is normal. The definition of a model associated to each feature offers the possibility to introduce a regularisation constraint. However, the chosen models are flexible enough to produce realistic contours for the mouth, the eyes and the eyebrows. This facial features segmentation is the first step of a set of multi-media applications.

Static Segmentation of the Lips for Follow-Up

“…In this article, we based ourselves on a variety of former researches [3][4][5][6][7][8] and we contributed in the following points:…”

“…• The manual site of the P3 point in a rigorous way on the lip, for the determination of higher Cupid according to the principle suggested by Eveno [7] • Localization of the corners P1 and P5 by dividing our selection rectangle into portions • Determination of the P6 point on the lower lip, by reducing the zone of search to a R 0 rectangle centered by the line which passes by P3 • The layout of cubic by the method of optimal slope We tested our application on a very large variety of speakers, unconstrained of lighting or make-up and we obtain satisfactory results.…”

“…Many authors choose the space RGB, which is best space to characterize the lips compared to the skin [7] . We define thereafter sizes to characterize and segment the lips.…”

Comparing a novel model based on the transferable belief model with humans during the recognition of partially occluded facial expressions

“…The first step in the Hammal et al (2007) facial expression model is the automatic extraction of the contours of the permanent facial features (eyes, eyebrows, and mouthVsee Hammal, Eveno, Caplier, & Coulon, 2006). However, the automatic segmentation of the mouth requires chromatic information and is not possible on the sole basis of the achromatic information of the face set used by Smith et al (Figure 3).…”

“…Thus, the small dispersion errors of the characteristic points do not significantly affect the classification process. This claim is based on the results of a quantitative evaluation using a ground truth corresponding to the results of the manual detection of the characteristic points by human experts (see Hammal et al, 2006).…”