Used Neural Networks - DCT for Face Identification for Windows?


Editors’ Review

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This Matlab source code implements DCT to reduce face image redundancy. Optimized coefficients are fed into a backpropagation neural network for fast, high-accuracy face identification. It achieves high recognition with minimal coefficients.
  • Pros

    • Reduces image information redundancy using DCT.
    • Preserves essential facial features with few coefficients.
    • Achieves high face recognition rates.
    • Utilizes backpropagation neural networks for classification.
    • Offers significantly faster face recognition.
  • Cons

    • Requires Matlab Image Processing Toolbox.
    • Requires Matlab Neural Network Toolbox.

Used Neural Networks - DCT for Face Identification for Windows?


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Full Specifications

GENERAL
Release
Latest update
Version
1
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows XP
  • Windows 2000
  • Windows 10
  • Windows NT
  • Windows 98
  • Windows ME
  • Windows 95
  • Windows 3
Additional Requirements
Windows 3.x/95/98/Me/NT/2000/XP/2003 Server, Matlab Image Processing Neural Network Toolboxes
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Developer’s Description

Minimize image information redundancy to avoid inefficiencies.

Neural Networks - DCT for Face Identification. Matlab source code. High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. Discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth.

We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are required.


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