Name
Abstract | ||
|---|---|---|
Deep learning provides a natural way to obtain feature representations from data without relying on hand-crafted descriptors. In this paper, we propose to learn deep feature representations using unsupervised and supervised learning in a cascaded fashion to produce generically descriptive yet class specific features. The proposed method can take full advantage of the availability of large-scale unlabeled data and learn discriminative features (supervised) from generic features (unsupervised). It is then applied to multiple essential facial regions to obtain multi-channel deep facial representations for face recognition. The efficacy of the proposed feature representations is validated on both controlled (i.e., extended Yale- B, Yale, and AR) and uncontrolled (PubFig) benchmark face databases. Experimental results show its effectiveness. |
Year | Venue | Field |
|---|---|---|
2015 | FE@NIPS | Facial recognition system,Pattern recognition,Computer science,Multi channel,Supervised learning,Artificial intelligence,Deep learning,Discriminative model,Machine learning |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-wen Chen | 1 | 61 | 8.06 |
| Melih S. Aslan | 2 | 42 | 6.52 |
| kunlei zhang | 3 | 2 | 0.37 |
| Thomas S. Huang | 4 | 27815 | 2618.42 |