Deformable models are an important tool in computer vision. A deformable model is a curve (in two dimensions) or a surface (in three dimensions), whose shape, position, and orientation are controlled through a set of parameters. They can represent manufactured objects, human faces and skeletons, and even bodies of fluid. Deformable models restrict the family of possible solutions in tracking an...

This paper studies two types of spatial relationships that can be learned from training examples for object recognition. The first one employs deformable relationships between object parts with a Gaussian model, while the second one describes pairwise relationships between pixel intensity values using Bayesian networks. We perform experiments on a human face dataset and a horse dataset, imposin...

One of the hardest problems in face recognition is dealing with facial expressions. Finding an expression-invariant representation of the face could be a remedy for this problem. We suggest treating faces as deformable surfaces in the context of Riemannian geometry, and propose to approximate facial expressions as isometries of the facial surface. This way, we can define geometric invariants of...

An essential question in many fields that deal with the nature of facial appearance is what are the invariants of the human face under various expressions. That is, can someone’s face have a unique description, that is independent of his or her facial expression. We previously suggested, without a proof, to treat facial expressions as isometries of deformable surfaces in the context of Riemanni...

In this paper, we present a new 3D face recognition approach. Full automation is provided through the use of advanced multi-stage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into...

In medical imagery, traditional deformable models often face substantial challenges due to fine structures and image complexity. Recently, based on magnetostatic theory, a new deformable model, namely MAC, is proposed for improving the ability of the active contour in dealing with complex geometries and segmentation difficulties. A Laplacian diffusion scheme is proposed in the MAC model to tack...

We propose a novel representation for characterizing image differences using a deformable technique for obtaining pixel-wise correspondences. This representation, which is based on a deformable 3D mesh in XYI-space, is then experimentally compared with two related correspondence methods: optical flow and intensity differences. Furthermore, we make use of a probabilistic similarity measure for d...

In the paper, a methodology for individual face synthesis using given orthogonal photos is proposed. And an integrated speech-driven facial animation system is presented. Firstly, in order to capture given subject’s personal facial configuration, a novel coarse-to-fine strategy based on facial texture and deformable template is proposed to localize some facial feature points in the image of fro...

Over the last decade 3D face models have been extensively used in many applications such as face recognition, facial animation and facial expression analysis. 3D Morphable Models (MMs) have become a popular tool to build and fit 3D face models to images. Critical to the success of MMs is the ability to learn a generic 3D face model. Towards that end, major limitations in the current MMs’ proces...

Model-based face analysis is a general paradigm with applications that include face recognition, expression recognition, lipreading, head pose estimation, and gaze estimation. A face model is first constructed from a collection of training data, either 2D images or 3D range scans. The face model is then fit to the input image(s) and the model parameters used in whatever the application is. Most...