Name
Abstract | ||
|---|---|---|
This paper proposes a method for face reconstruction that makes use of only a small set of feature points. Faces can be modeled by forming linear combinations of prototypes of shape and texture information. With the shape and texture information at the feature points alone, we can achieve only an approximation to the deformation required. In such an under-determined condition, we find an optimal solution using a simple least square minimization method. As experimental results, we show well-reconstructed 2D faces even from a small number of feature points. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45482-9_30 | Biologically Motivated Computer Vision |
Keywords | Field | DocType |
linear combination,feature point,small set,small number,square minimization method,feature points,texture information,face reconstruction,optimal solution,under-determined condition,least square | Least squares,Linear combination,Computer science,Minification,Artificial intelligence,Small set,Iterative reconstruction,Computer vision,Discrete mathematics,Facial recognition system,Pattern recognition,Image texture,Feature (computer vision) | Conference |
Volume | ISSN | ISBN |
1811 | 0302-9743 | 3-540-67560-4 |
Citations | PageRank | References |
3 | 0.55 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bon-Woo Hwang | 1 | 177 | 16.33 |
| Volker Blanz | 2 | 3538 | 308.38 |
| Thomas Vetter | 3 | 4528 | 529.79 |
| Seong-Whan Lee | 4 | 3756 | 343.90 |