Icon of program: ICA Face Recognition

ICA Face Recognition for Windows

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Editors’ Review

Download.com staff
This MATLAB source code implements Independent Component Analysis for face recognition, surpassing PCA in handling high-order relationships and variations.

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  • Pros

    • Utilizes ICA for superior face recognition.
    • Captures high-order statistical dependencies.
    • Handles variations in sessions and expressions effectively.
    • Based on optimal information transfer principles.
    • Provides statistically independent outputs.

  • Cons

    • Requires MATLAB environment.
    • Focuses solely on ICA methodology.
    • Technical description may be dense for novices.
Icon of program: ICA Face Recognition

ICA Face Recognition for Windows

Download now

Used ICA Face Recognition for Windows?

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Full Specifications

GENERAL
Release
Latest update
Version
1.0
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows NT
  • Windows Server
  • Windows 98
  • Windows 7
  • Windows 8
  • Windows XP
  • Windows ME
  • Windows 10
  • Windows 2003
  • Windows Vista
  • Windows 2000
Additional Requirements
Matlab
POPULARITY
Total Downloads
168
Downloads Last Week
1
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Developer’s Description

By Luigi Rosa
Get an ICA Face Recognition Matlab source code.
In a task such as face recognition, much of the important information may be contained in the high-order relationships among the image pixels. A number of face recognition algorithms employ principal component analysis (PCA), which is based on the second-order statistics of the image set, and does not address high-order statistical dependencies such as the relationships among three or more pixels. Independent component analysis (ICA) is a generalization of PCA which separates the high-order moments of the input in addition to the second-order moments. ICA was performed on a set of face images by an unsupervised learning algorithm derived from the principle of optimal information transfer through sigmoidal neurons. The algorithm maximizes the mutual information between the input and the output, which produces statistically independent outputs under certain conditions. ICA representation was superior to representations based on principal components analysis for recognizing faces across sessions and changes in expression.
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