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Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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In the absence of...
Vector or Cross Product01:17

Vector or Cross Product

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Fast Fourier Transform01:10

Fast Fourier Transform

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Deconvolution

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Related Experiment Videos

Multiview face recognition: from TensorFace to V-TensorFace and K-TensorFace.

Chunna Tian1, Guoliang Fan, Xinbo Gao

  • 1Video and Image Processing System Laboratory, School of Electronic Engineering, Xidian University, Xi'an 710071, China. chnatian@xidian.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 10, 2012
PubMed
Summary
This summary is machine-generated.

New TensorFace models (V-TensorFace and K-TensorFace) improve multiview face recognition by preserving local distances and manifold structures. These methods effectively represent unseen views and outperform existing approaches.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Uncontrolled environments introduce variations in face images (view, illumination, expression).
  • Tensor analysis aids in understanding facial variations, but existing TensorFace models struggle with nonlinear view subspaces.
  • Multiview face recognition requires robust methods to handle diverse facial appearances.

Purpose of the Study:

  • To address the limitations of TensorFace in representing nonlinear view subspaces.
  • To propose novel methods, V-TensorFace and K-TensorFace, for enhanced multiview face recognition.
  • To develop a unified framework for generalizing TensorFace and its variants.

Main Methods:

  • Introduced V-TensorFace, utilizing a latent view manifold to preserve local distances in multiview face spaces.
  • Proposed K-TensorFace to preserve latent manifold structures in image space for multiview face recognition.
  • Developed a unified framework encompassing TensorFace, V-TensorFace, and K-TensorFace.
  • Implemented an expectation-maximization-like algorithm for iterative estimation of identity and view parameters.

Main Results:

  • The manifold construction method demonstrated effectiveness on the PIE database.
  • V- and K-TensorFace significantly outperformed view-based principal component analysis and other state-of-the-art methods.
  • Experiments on Weizmann and Oriental Face databases confirmed the superiority of the proposed methods for multiview face recognition.

Conclusions:

  • V-TensorFace and K-TensorFace offer improved generative models for unseen view representation in face recognition.
  • The proposed unified framework provides a generalized approach to TensorFace methods.
  • The developed iterative algorithm effectively estimates identity and view parameters for unknown views.