This work presents a robust, and low-cost framework for real-time marker based 3-D human expression modeling using off-the-shelf stereo web-cameras and inexpensive adhesive markers applied to the face. The system has low computational requirements, runs on standard hardware, and is portable with minimal setup time and no training. It does not require a controlled lab environment (lighting or setup) and is robust under varying conditions, i.e. illumination, facial hair, or skin tone variation. Stereo web-cameras perform 3-D marker tracking to obtain head rigid motion and the non-rigid motion of expressions. Tracked markers are then mapped onto a 3-D face model with a virtual muscle animation system. Muscle inverse kinematics update muscle contraction parameters based on marker motion in order to create a virtual character's expression performance. The parametrization of the muscle-based animation encodes a face performance with little bandwidth. Additionally, a radial basis function mapping approach was used to easily remap motion capture data to any face model. In this way the automated creation of a personalized 3-D face model and animation system from 3-D data is elucidated. The expressive power of the system and its ability to recognize new expressions was evaluated on a group of test subjects with respect to the six universally recognized facial expressions. Results show that the use of abstract muscle definition reduces the effect of potential noise in the motion capture data and allows the seamless animation of any virtual anthropomorphic face model with data acquired through human face performance.
Abstract. Our goal is to obtain three-dimensional measurements of craniofacial morphology in a healthy population, using standard landmarks established by a physical-anthropology specialist and picked from computer reconstructions of the face of each subject. To do this, we designed a multi-stereo vision system that will be used to create a data base of human faces surfaces from a healthy population, for eventual applications in medicine, forensic sciences and anthropology. The acquisition process consists of obtaining the depth map information from three points of views, each depth map is obtained from a calibrated pair of cameras. The depth maps are used to build a complete, frontal, triangular-surface representation of the subject face. The triangular surface is used to locate the landmarks and the measurements are analyzed with a MATLAB script. The classification of the subjects was done with the aid of a specialist anthropologist that defines specific subject indices, according to the lengths, areas, ratios, etc., of the different structures and the relationships among facial features. We studied a healthy population and the indices from this population will be used to obtain representative averages that later help with the study and classification of possible pathologies.
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