Name
Abstract | ||
|---|---|---|
As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples. However, most existing efforts to age estimation usually cope with the face samples individually, which do not take full advantage of the temporal structure and contextual structure of the face samples. In this letter, we propose an... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LSP.2016.2602538 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Face,Estimation,Optimization,Predictive models,Feature extraction,Electronic mail,Manifolds | Feature transformation,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Regression problems,Computer vision feature extraction,Smoothness,Machine learning,Pattern recognition (psychology) | Journal |
Volume | Issue | ISSN |
23 | 12 | 1070-9908 |
Citations | PageRank | References |
1 | 0.35 | 29 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou-zhou He | 1 | 23 | 2.12 |
| Xi Li | 2 | 1850 | 137.71 |
| Zhongfei (Mark) Zhang | 3 | 2451 | 164.30 |
| Yaqing Zhang | 4 | 48 | 6.36 |
| Jun Xiao | 5 | 513 | 50.95 |
| Xue Zhou | 6 | 51 | 13.31 |