Name
Abstract | ||
|---|---|---|
In recent years, convolutional neural networks have proven to be a highly efficient approach for face recognition. In this paper, we develop such a framework to learn a robust face verification in an unconstrained environment using aggressive data augmentation. Our objective is to learn a deep face representation from large-scale data with massive noisy and occluded face. Besides, we add an adaptive fusion of softmax loss and center loss as supervision signals, which are helpful to improve the performance and to conduct the final classification. The experiment results show that the suggested system achieves comparable performances with other state-of-the-art methods on the Labeled Faces in the Wild and YouTube face verification tasks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00371-020-01794-9 | The Visual Computer |
Keywords | DocType | Volume |
Face recognition, Deep learning, Data augmentation | Journal | 37 |
Issue | ISSN | Citations |
2 | 0178-2789 | 2 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hana Ben Fredj | 1 | 2 | 0.70 |
| Safa Bouguezzi | 2 | 2 | 1.38 |
| Chokri Souani | 3 | 41 | 8.75 |