Eigenfaces vs. Fisherfaces: recognition using class specific linearprojection
Belhumeur, P.N.; Hespanha, J.P.; Kriegman, D.J.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 19, Issue 7, Jul 1997 Page(s):711 - 720
Digital Object Identifier 10.1109/34.598228
Summary: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. Our projection method is based
on Fisher'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 “Fisherface” method has error
rates that are lower than those of the eigenface technique for tests on
the Harvard and Yale face databases
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