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FisherFaces for Face Matching for Windows

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Used FisherFaces for Face Matching for Windows?

Editors’ Review

Download.com staff
This Matlab source code implements a face recognition algorithm that is robust to lighting and expression variations. It utilizes a 3D linear subspace projection for accurate matching.

  • Pros

    • Insensitive to large variations in lighting direction
    • Handles variations in facial expression
    • Projects images into a subspace for matching
    • Leverages pattern classification approach
    • Developed by advancedsourcecode.com

  • Cons

    • Requires Matlab Image Processing Toolbox
    • Assumes Lambertian surface properties for ideal results
    • May deviate from linear subspace due to self-shadowing
    • Does not explicitly model deviation from linear subspace
Icon of program: FisherFaces for Face Matc…

FisherFaces for Face Matching for Windows

Download now

Used FisherFaces for Face Matching for Windows?

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Full Specifications

GENERAL
Release
Latest update
Version
1
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows NT
  • Windows 95
  • Windows 98
  • Windows XP
  • Windows 2000
  • Windows 10
  • Windows 3
  • Windows ME
Additional Requirements
Windows 3.x/95/98/Me/NT/2000/XP/2003 Server, Matlab Image Processing Toolbox.
POPULARITY
Total Downloads
6,185
Downloads Last Week
6
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Developer’s Description

By Luigi Rosa
Create and modify faces in 3D linear subspace.
FisherFaces for Face Matching 1 Matlab Source Code. We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Matlab Image Processing Toolbox is required.
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