Name
Abstract | ||
|---|---|---|
Deep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source–target–source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00521-016-2325-5 | Neural Computing and Applications |
Keywords | Field | DocType |
Transfer learning, Deep neural networks, Source–target–source, Optimization, Cross-sensor biometrics | Inductive transfer,Transfer of learning,Artificial intelligence,Biometrics,Image database,Multi-source,Machine learning,Deep neural networks,Mathematics | Journal |
Volume | Issue | ISSN |
28 | 9 | 1433-3058 |
Citations | PageRank | References |
7 | 0.51 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chetak Kandaswamy | 1 | 46 | 4.51 |
| João C. Monteiro | 2 | 38 | 3.83 |
| Luis Silva | 3 | 9 | 1.90 |
| Jaime S. Cardoso | 4 | 543 | 68.74 |