Sciweavers

Share
FGR
2000
IEEE

From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination

10 years 4 months ago
From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination
Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearance-based methods which, unlike previous appearance-based approaches, require only a small set of training images to generate a rich representation that models this variability. Speci cally, from as few as three images of an object in xed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was speci cally gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods.
Athinodoros S. Georghiades, Peter N. Belhumeur, Da
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where FGR
Authors Athinodoros S. Georghiades, Peter N. Belhumeur, David J. Kriegman
Comments (0)
books