Cross domain meta-network for sketch face recognition
| Autor: | Shao Yuying, Cao Lin, Chen Changwu, Du Kangning |
|---|---|
| Jazyk: | English<br />French |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | MATEC Web of Conferences, Vol 336, p 06007 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2261-236X 20213360 |
| DOI: | 10.1051/matecconf/202133606007 |
| Popis: | Because of the large modal difference between sketch image and optical image, and the problem that traditional deep learning methods are easy to overfit in the case of a small amount of training data, the Cross Domain Meta-Network for sketch face recognition method is proposed. This method first designs a meta-learning training strategy to solve the small sample problem, and then proposes entropy average loss and cross domain adaptive loss to reduce the modal difference between the sketch domain and the optical domain. The experimental results on UoM-SGFS and PRIP-VSGC sketch face data sets show that this method and other sketch face recognition methods. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|