Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. An algorithm that performs detection, extraction, and evaluation of these facial expressions will allow for automatic recognition of human emotion in images and videos. Presented here is a hybrid feature extraction and facial expression recognition method that utilizes Viola-Jones...

synthetic face’s behaviors must precisely conform to those of a real one. However, facial surface points, being nonlinear and without rigid body properties, have quite complex action relations. During speaking and pronunciation, facial motion trajectories between articulations, called coarticulation effects, also prove nonlinear and depend on preceding and succeeding articulations. Performance-...

synthetic face’s behaviors must precisely conform to those of a real one. However, facial surface points, being nonlinear and without rigid body properties, have quite complex action relations. During speaking and pronunciation, facial motion trajectories between articulations, called coarticulation effects, also prove nonlinear and depend on preceding and succeeding articulations. Performance-...

Feature extraction plays an important role in facial expression recognition. Canonical correlation analysis (CCA), which studies the correlation between two random vectors, is a major linear feature extraction method based on feature fusion. Recent studies have shown that facial expression images often reside on a latent nonlinear manifold. However, either CCA or its kernel version KCCA, which ...

We propose a Simplified Generic Elastic Model (S-GEM) which intends to construct a 3D face from a given 2D face image by making use of a set of general human traits viz., Gender, Ethnicity and Age (GEA). We hypothesise that the variations inherent on the depth information for individuals are significantly mitigated by narrowing down the target information via a selection of specific GEA traits....

Preparing a facial mesh to be animated requires a laborious manual rigging process. The rig specifies how the input animation data deforms the surface and allows artists to manipulate a character. We present a method that automatically rigs a facial mesh based on Radial Basis Functions (RBF) and linear blend skinning approach. Our approach transfers the skinning parameters (feature points and t...

This chapter presents our research on real-time speech-driven face animation. First, a visual representation, called Motion Unit (MU), for facial deformation is learned from a set of labeled face deformation data. A facial deformation can be approximated by a linear combination of MUs weighted by the corresponding MU parameters (MUPs), which are used as the visual features of facial deformation...

The paper proposes a robust framework for 3D facial movement tracking in real time using a monocular camera. It is designed to estimate the 3D face pose and local facial animation such as eyelid movement and mouth movement. The framework firstly utilizes the Discriminative Shape Regression method to locate the facial feature points on the 2D image and fuses the 2D data with a 3D face model usin...

Facial feature extraction consists in localizing the most characteristic face components (eyes, nose, mouth, etc.) within images that depict human faces. This step is essential for the initialization of many face processing techniques like face tracking, facial expression recognition or face recognition. Among these, face recognition is a lively research area where it has been made a great effo...