Name
Abstract | ||
|---|---|---|
Deformable model fitting to high-resolution facial images has been extensively studied for over two decades. However, due to the ill-posed problem caused by low-resolution images, most existing work cannot be applied directly and degrades quickly as the resolution decreases. To address this issue, this paper extends the Constrained Local Model (CLM) to a multi-resolution model consisting of a 4-level patch pyramid, and deploys various feature descriptors for the local patch experts as well. We evaluate the proposed work on the BioID, the MUCT and the Multi-PIE datasets. Superior results are achieved on almost all resolution levels, demonstrating the effectiveness and necessity of our resolution-aware approach for the low-resolution fitting. Improved performance of patch models employing several feature combinations over the single intensity feature under different conditions is also presented. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/AVSS.2013.6636682 | AVSS |
Keywords | Field | DocType |
deformable model fitting,multipie datasets,face recognition,resolution-aware constrained local model,image resolution,low resolution,high-resolution facial images,feature mixture,low-resolution fitting,constrained local model,clm,local experts,face model | Facial recognition system,Computer vision,Pattern recognition,Computer science,Active appearance model,Robustness (computer science),Pyramid,Artificial intelligence,Image resolution | Conference |
Citations | PageRank | References |
2 | 0.36 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengchao Qu | 1 | 34 | 5.89 |
| Eduardo Monari | 2 | 68 | 6.47 |
| Tobias Schuchert | 3 | 93 | 12.21 |