Name
Abstract | ||
|---|---|---|
Illumination spaces capture how the appearances of human faces vary under changing illumination. This work models illumination spaces as points on a Grass- mann manifold and uses distance measures on this mani- fold to show that every person in the CMU-PIE and Yale data sets has a unique and identifying illumination space. This suggests that variations under changes in illumina- tion can be exploited for their discriminatory information . As an example, when face recognition is cast as match- ing sets of face images to sets of face images, subjects in the CMU-PIE and Yale databases can be recognized with 100% accuracy. |
Year | Venue | Keywords |
|---|---|---|
2006 | IPCV | face recognition |
Field | DocType | Citations |
Computer vision,Facial recognition system,Data set,Grassmannian,Artificial intelligence,Manifold,Mathematics,Distance measures | Conference | 5 |
PageRank | References | Authors |
0.65 | 16 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jen-Mei Chang | 1 | 42 | 3.26 |
| J. Ross Beveridge | 2 | 1716 | 190.52 |
| Bruce A. Draper | 3 | 2001 | 207.57 |
| Michael Kirby | 4 | 137 | 14.40 |
| Holger Kley | 5 | 5 | 0.65 |
| Chris Peterson | 6 | 68 | 10.93 |