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3D model of a human face. Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition.
The 3D Morphable Model (3DMM) is a general framework that has been applied to various objects other than faces, e.g., the whole human body, [3] [4] specific body parts, [5] [6] and animals. [ 7 ] 3DMMs were first developed to solve vision tasks by representing objects in terms of the prior knowledge that can be gathered from that object class.
Frederic Ira Parke is an American computer graphics researcher and academic. He did early work on animated computer renderings of human faces. Parke graduated from the University of Utah with a BS degree in physics in 1965.
2.5D (visual perception) offers an automatic approach to making human face models. It analyzes a range data set and a color perception image. The sources are analyzed separately to identify the anatomical sites of features, craft the geometry of the face and produce a volumetric facial model. [8]
The tool is specifically designed for the modeling of virtual 3D human models, with a simple and complete pose system that includes the simulation of muscular movement. The interface is easy to use, with fast and intuitive access to the numerous parameters required in modeling the human form.
In 1971 Henri Gouraud made the first CG geometry capture and representation of a human face. Modeling was his wife Sylvie Gouraud. The 3D model was a simple wire-frame model and he applied the Gouraud shader he is most known for to produce the first known representation of human-likeness on computer. [3] [4]
Any human face can be considered to be a combination of these standard faces. For example, one's face might be composed of the average face plus 10% from eigenface 1, 55% from eigenface 2, and even −3% from eigenface 3. Remarkably, it does not take many eigenfaces combined together to achieve a fair approximation of most faces.
Chernoff faces, invented by applied mathematician, statistician and physicist Herman Chernoff in 1973, display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth and nose represent values of the variables by their shape, size, placement and orientation.