Name
Abstract | ||
|---|---|---|
This paper discusses Gaussian decomposition of facial images for robust recognition. While it cannot sufficiently extract an effective component, it can decompose an image into two effective components, the filtered image and its residual. The Gaussian component represents rough information for a lighting condition and small individuality. The residual represents individuality and the other information including small noise. The two components complement each other and they are evaluated independently in the framework of eigenface method. The image decomposition can also collaborate with parallel partial projections for robust recognition. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11612032_12 | ACCV (1) |
Keywords | Field | DocType |
gaussian decomposition,effective component,rough information,facial image,robust face recognition,small individuality,image decomposition,filtered image,robust recognition,gaussian component,small noise,face recognition | Computer vision,Facial recognition system,Residual,Eigenface,Pattern recognition,Computer science,Image processing,Gaussian,Artificial intelligence,Gaussian process,Principal component analysis | Conference |
Volume | ISSN | ISBN |
3851 | 0302-9743 | 3-540-31219-6 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fumihiko Sakaue | 1 | 46 | 19.84 |
| Takeshi Shakunaga | 2 | 192 | 43.46 |