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Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1064)

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 direction lie in a 3-D linear subspace of the high dimensional feature space — if the face is a Lambertian surface without self-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 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 significantly lower than those of the Eigenface technique when tested on the same database.

Keywords

  • Facial Expression
  • Face Recognition
  • Face Image
  • Linear Subspace
  • Face Recognition Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

J. Hespanha was supported by NSF Grant ECS-9206021, AFOSR Grant F49620-94-1-0181, and ARO Grant DAAH04-95-1-0114.

D. Kriegman was supported by NSF under an NYI, IRI-9257990 and by ONR N00014-93-1-0305

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© 1996 Springer-Verlag Berlin Heidelberg

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Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J. (1996). Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015522

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  • DOI: https://doi.org/10.1007/BFb0015522

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