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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.
The feature vector can then be further processed for many different tasks. For example, to identify the face, one can compare it against a list of feature vectors of known faces, and identify the face with the most similar feature vector. DeepFace uses fiducial point detectors based on existing databases to direct the alignment of faces.
A face shape of vertices is defined as the vector containing the 3D coordinates of the vertices in a specified order, that is . A shape space is regarded as a d {\textstyle d} -dimensional space that generates plausible 3D faces by performing a lower-dimensional ( d ≪ n {\textstyle d\ll n} ) parametrization of the database. [ 2 ]
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 dimension of this vector space is the number of pixels. The eigenvectors of the covariance matrix associated with a large set of normalized pictures of faces are called eigenfaces; this is an example of principal component analysis. They are very useful for expressing any face image as a linear combination of some of them.
As these are vector graphics, the images can be scaled to any size, large or small, without loss of quality. Inkscape is a free program used to edit vector graphics. Inkscape provides a graphical user interface for the editing of such diagrams, using the standard Scalable Vector Graphics (SVG) format. [1]
Faces are arranged using vectors from this norm, with the vector’s parameters of length and direction determined by the distinctiveness and features of the face respectively. [ 3 ] In the exemplar-based model, faces are encoded as individual points in the space, rather than as vectors relative to a norm face.
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.
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