Method of modelling facial action units using partial differential equations

NoIn this paper we discuss a novel method of mathematically modelling facial action units for accurate representation of human facial expressions in 3- dimensions. Our method utilizes the approach of Facial Action Coding System (FACS). It is based on a boundary-value approach, which utilizes a solution to a fourth order elliptic Partial Differential Equation (PDE) subject to a suitable set of boundary conditions. Here the PDE surface generation method for human facial expressions is utilized in order to generate a wide variety of facial expressions in an efficient and realistic way. For this purpose, we identify a set of boundary curves corresponding to the key features of the face which in turn define a given facial expression in 3-dimensions. The action units (AUs) relating to the FACS are then efficiently represented in terms of Fourier coefficients relating to the boundary curves which enables us to store both the face and the facial expressions in an efficient way

Geodesic Active Contours with Combined Shape and Appearance Priors

Abstract. We present a new object segmentation method that is based on geodesic active contours with combined shape and appearance priors. It is known that using shape priors can significantly improve object segmentation in cluttered scenes and occlusions. Within this context, we add a new prior, based on the appearance of the object, (i.e., an image) to be segmented. This method enables the appearance pattern to be incorporated within the geodesic active contour framework with shape priors, seeking for the object whose boundaries lie on high image gradients and that best fits the shape and appearance of a reference model. The output contour results from minimizing an energy functional built of these three main terms. We show that appearance is a powerful term that distinguishes between objects with similar shapes and capable of successfully segment an object in a very cluttered environment where standard active contours (even those with shape priors) tend to fail.

Facial Expression Recognition Using Image Motion

Introduction The communicative power of the face makes machine understanding and recognition of human expression an important problem in computer vision. There is a significant amount of research on facial expressions in computer vision and computer graphics [11, 23]. Perhaps the most fundamental problem in this area is how to categorize active and spontaneous facial expressions to extract information about the underlying emotional states [6, 26]. Ekman and Friesen [10] have produced the most widely used system for describing visually distinguishable facial movements. This system, called the Facial Action Coding System or FACS, is based on the enumeration of all "action units" of a face which cause facial movements. As some muscles give rise to more than one action unit, the correspondence between action units and muscle units is approximate. However, it is widely recognized that the lack of temporal and detailed spatial information (both loca