FisherFaces for Face Matching Icon

FisherFaces for Face Matching

We develop a face recognition algorithm which is insensitive to large variation

FisherFaces for Face MatchingOverview

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 spaceâ??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. Our projection method is based on Fisherâ??s Linear Discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has Similar computational requirements. Yet, extensive experimental results demonstrate that the proposed â??Fisherfaceâ?? method has error rates that are lower than those of the Eigenface technique for tests on the Harvard and Yale Face Databases. A complete list of public face databases is available

NEW

Fixed some bugs.

FisherFaces for Face MatchingInformation

Version
1.0
Date
09.01.08
License
Free
Language
English
File Size
300KB
Developer
SubCategory
Operating Systems
Windows 2003, XP, 2000, 98, Me, NT
System Requirements
No additional system requirements.
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