Name
Abstract | ||
|---|---|---|
In this paper, we present a label-sensitive deep metric learning (LSDML) approach for facial age estimation. Motivated by the fact that human age labels are chronologically correlated, our proposed LSDML aims to seek a series of hierarchical nonlinear transformations by deep residual network to project face samples to a latent common space, where the similarity of face pairs is equivalently isoton... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIFS.2017.2746062 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
Face,Measurement,Estimation,Correlation,Learning systems,Aging,Robustness | Data set,Nonlinear system,Computer science,Artificial intelligence,Benchmarking,Manifold,Residual,Computer vision,Tree traversal,Pattern recognition,Subspace topology,Correlation,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 2 | 1556-6013 |
Citations | PageRank | References |
8 | 0.49 | 59 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Liu | 1 | 113 | 10.67 |
| Jiwen Lu | 2 | 3105 | 153.88 |
| Jianjiang Feng | 3 | 814 | 62.59 |
| Jie Zhou | 4 | 2103 | 190.17 |