Name
Abstract | ||
|---|---|---|
Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: A large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TPAMI.2005.92 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
k image,s,face recognition,acquiring linear subspaces,diffuse lighting,basis vector,basis image,acquired image,low-dimensional linear space,resulting subspace,different lighting condition,variable lighting,physical lighting,3d reconstruction,photometry,image reconstruction,shadow mapping,image recognition,light field,point source,spherical harmonic,lighting,spherical harmonics,computer vision,principal component analysis,face,image analysis,linear models,artificial intelligence,linear space,algorithms | Iterative reconstruction,Computer vision,Facial recognition system,Pattern recognition,Subspace topology,Computer science,Linear space,Linear subspace,Artificial intelligence,Real image,Basis (linear algebra),3D reconstruction | Journal |
Volume | Issue | ISSN |
27 | 5 | 0162-8828 |
Citations | PageRank | References |
1066 | 37.79 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuang-chih Lee | 1 | 2297 | 104.80 |
| Jeffrey Ho | 2 | 2190 | 101.78 |
| David Kriegman | 3 | 7693 | 451.96 |