Perceptual Signatures:

Biometric Computing using Perceptual Signatures: Perceptual signatures distilled from biometric data is fundamental to humancentric technologies such as the burgeoning security industry, but when directed at ourselves, biometric technologies can serve as reflectors that enhance our selfawareness, understanding, and health, and they can facilitate our interaction with each other and computers.

Stony Brook University (SUNY, Stony Brook):
Massachusetts Institute of Technology:
New York University:
Multilinear Projection
Generalizing concepts from linear (matrix) algebra, we define the moden identity tensor and the moden pseudoinverse tensor and we employ them to develop a multilinear projection algorithm in order to performing recognition in the tensor algebraic framework. Multilinear projection simultaneously projects an unlabeled test image into multiple constituent mode spaces, associated with image formation, in order to infer the mode labels. Multilinear projection is applied to unconstrained facial image recognition, where the mode labels are person identity, viewpoint, illumination, etc.
Multilinear (Tensor) Independent Component Analysis
Independent Component Analysis (ICA) minimizes the statistical dependence of the representational components of a training image ensemble, but it cannot distinguish between the different factors related to scene structure, illumination and imaging, which are inherent to image formation. We introduce a nonlinear, multifactor model that generalizes ICA. A Multilinear ICA (MICA) model of image ensembles learns the statistically independent components of multiple factors. Whereas ICA employs linear (matrix) algebra, MICA exploits multilinear (tensor) algebra. In the context of facial image ensembles, we demonstrate that the statistical regularities learned by MICA capture information that improves automatic face recognition.
An essential goal of computer graphics is photorealistic rendering, the synthesis of images of virtual scenes visually indistinguishable from those of natural scenes. Unlike traditional modelbased rendering, whose photorealism is limited by model complexity, an emerging and highly active research area known as {\it imagebased rendering} eschews complex geometric models in favor of representing scenes by ensembles of example images. These are used to render novel photoreal images of the scene from arbitrary viewpoints and illuminations, thus decoupling rendering from scene complexity. The challenge is to develop structured representations in highdimensional image spaces that are rich enough to capture important information for synthesizing new images, including details such as selfocclusion, selfshadowing, interreflections, and subsurface scattering.
TensorTextures, a new imagebased texture mapping technique, is a rich generative model that, from a sparse set of example images, learns the interaction between viewpoint, illumination, and geometry that determines detailed surface appearance. Mathematically, TensorTextures is a nonlinear model of texture image ensembles that exploits tensor algebra and the Nmode SVD to learn a representation of the bidirectional texture function (BTF) in which the multiple constituent factors, or modes—viewpoints and illuminations—are disentangled and represented explicitly.
TensorFaces – Tensor Decomposition of Image Ensembles
The goal of machine vision is automated image understanding and object recognition by a computer. Recent events have redoubled interest in biometrics and the application of computer vision technologies to nonobtrusive identification, surveillance, tracking, etc. Face recognition is a difficult problem for computers. This is due largely to the fact that images are the composite consequence of multiple factors relating to scene structure (i.e., the location and shapes of visible objects), illumination (i.e., the location and types of light sources), and imaging (i.e., viewpoint, viewing direction and camera characteristics). Multiple factors can confuse and mislead an automated recognition system. In addressing this problem, we take advantage of the assets of multilinear algebra, the algebra of higherorder tensors, to obtain a parsimonious representation that separates the various constituent factors. Our new representation of facial images, called TensorFaces, leads to improved recognition algorithms for use in the aforementioned applications.
Human Motion Signatures – 3Mode Data Tensor Analysis
Given motion capture samples of Charlie Chaplin’s walk, is it possible to synthesize other motions—say, ascending or descending stairs—in his distinctive style? More generally, in analogy with handwritten signatures, do people have characteristic motion signatures that individualize their movements? If so, can these signatures be extracted from example motions? Furthermore, can extracted signatures be used to recognize, say, a particular individual’s walk subsequent to observing examples of other movements produced by this individual?
We have developed an algorithm that extracts motion signatures and uses them in the animation of graphical characters. The mathematical basis of our algorithm is a statistical numerical technique known as nmode analysis. For example, given a corpus of walking, stair ascending, and stair descending motion data collected over a group of subjects, plus a sample walking motion for a new subject, our algorithm can synthesize never before seen ascending and descending motions in the distinctive style of this new individual.
Adaptive mesh models for the nonuniform sampling and reconstruction of visual data. Adaptive meshes are dynamic models assembled from nodal masses connected by adjustable springs. Acting as mobile sampling sites, the nodes observe interesting properties of the input data, such as intensities, depths, gradients, and curvatures. The springs automatically adjust their stiffnesses based on the locally sampled information in order to concentrate nodes near rapid variations in the input data. The representational power of an adaptive mesh is enhanced by its ability to optimally distribute the available degrees of freedom of the reconstructed model in accordance with the local complexity of the data.
We developed open adaptive mesh and closed adaptive shell surfaces based on triangular or rectangular elements. We propose techniques for hierarchically subdividing polygonal elements in adaptive meshes and shells. We also devise a discontinuity detection and preservation algorithm suitable for the model. Finally, motivated by (nonlinear, continuous dynamics, discrete observation) Kalman filtering theory, we generalize our model to the dynamic recursive estimation of nonrigidly moving surfaces.
Source:
http://web.media.mit.edu/~maov/index.html
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