Name
Abstract | ||
|---|---|---|
Increasing inter-class variance and shrinking intra-class distance are two main concerns and efforts in face recognition. In this paper, we propose a new loss function termed inter-class angular margin (IAM) loss aiming to enlarge the inter-class variance. Instead of restricting the inter-class margin to be a constant in existing methods, our IAM loss adaptively penalizes smaller inter-class angles more heavily and successfully makes the angular margin between classes larger, which can significantly enhance the discrimination of facial features. The IAM loss can be readily introduced as a regularization term for the widely-used Softmax loss and its recent variants to further improve their performances. We also analyze and verify the appropriate range of the regularization hyper-parameter from the perspective of backpropagation. For illustrative purposes, our model is trained on CASIA-WebFace and tested on the LFW, CFP, YTF and MegaFace datasets; the experimental results show that the IAM loss is quite effective to improve state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.image.2019.115636 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Face recognition,IAM loss,Inter-class variance,Intra-class distance,Softmax loss | Facial recognition system,Computer vision,Pattern recognition,Softmax function,Computer science,Regularization (mathematics),Artificial intelligence,Backpropagation | Journal |
Volume | ISSN | Citations |
80 | 0923-5965 | 2 |
PageRank | References | Authors |
0.40 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| WM | 1 | 221 | 34.28 |
| Jingna Sun | 2 | 2 | 0.74 |
| Riqiang Gao | 3 | 2 | 2.43 |
| Jing-Hao Xue | 4 | 393 | 46.48 |
| QM | 5 | 464 | 72.05 |