Automatic facial expression tracking for 4D range scans

This paper presents a fully automatic approach of spatio-temporal facial expression tracking for 4D range scans without any manual interventions (such as specifying landmarks). The approach consists of three steps: rigid registration, facial model reconstruction, and facial expression tracking. A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registration between a template facial model and a range scan with consideration of the scale problem. A deformable model, physically based on thin shells, is proposed to faithfully reconstruct the facial surface and texture from that range data. And then the reconstructed facial model is used to track facial expressions presented in a sequence of range scans by the deformable model

Automatic 3D facial model and texture reconstruction from range scans

This paper presents a fully automatic approach to fitting a generic facial model to detailed range scans of human faces to reconstruct 3D facial models and textures with no manual intervention (such as specifying landmarks). A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registrations between the generic model and the range scans with different sizes. And then a new template-fitting method, formulated in an optmization framework of minimizing the physically based elastic energy derived from thin shells, faithfully reconstructs the surfaces and the textures from the range scans and yields dense point correspondences across the reconstructed facial models. Finally, we demonstrate a facial expression transfer method to clone facial expressions from the generic model onto the reconstructed facial models by using the deformation transfer technique

3-D facial expression representation using B-spline statistical shape model

Effective representation and recognition of human faces are essential in a number of applications including human-computer interaction (HCI), bio-metrics or video conferencing. This paper presents initial results obtained for a novel method of 3-D facial expressions representation based on the shape space vector of the statistical shape model. The statistical shape model is constructed based on the control points of the B-spline surfaces of the train-ing data set. The model fitting for the data is achieved by a modified iterative closest point (ICP) method with the surface deformations restricted to the es-timated shape space. The proposed method is fully automated and tested on the synthetic 3-D facial data with various facial expressions. Experimental results show that the proposed 3-D facial expression representation can be potentially used for practical applications

A statistical shape model for deformable surface

This short paper presents a deformable surface registration scheme which is based on the statistical shape modelling technique. The method consists of two major processing stages, model building and model fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and matched to the new data by a modified iterative closest point (ICP) registration process. The proposed method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can achieve a reasonable result on surface registration, and can be used for patient position monitoring in radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and neck patients by analysis of facial articulation