Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

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Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection (1997)

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by Peter N. Belhumeur , João P. Hespanha , David J. Kriegman

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2309 - 17 self

BibTeX

@MISC{Belhumeur97eigenfacesvs.,
    author = {Peter N. Belhumeur and João P. Hespanha and David J. Kriegman},
    title = {Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection},
    year = {1997}
}

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Abstract

We develop a face recognition algorithm which is insensitive to gross 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 3-D 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

Keyphrases

eigenfaces v class specific linear projection lambertian surface facial expression low-dimensional subspace 3-d linear subspace projection method high-dimensional space pattern classification approach separated class large deviation particular face linear discriminant gross variation linear subspace severe variation high dimensional image space face recognition algorithm

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