Name
Abstract | ||
|---|---|---|
Although face recognition has been widely applied in many areas, pose-robust face recognition is still a challenging topic due to the large pose variations in real scenes. In this paper, we propose to learn the pose-robust face representation by normalizing the profile face in feature level directly and jointly considering both intra-class compactness and inter-class separability. Our approach minimizes the angular distance between the profile face and the positive frontal anchor. And it maximizes the angular distance between the profile face and the negative frontal anchor simultaneously. Furthermore, we modify the Triplet loss and derive the Triplet Angular loss to guarantee the intra-class compactness and the inter-class separability in angular space. In this way, the faces under varying poses can cluster compactly to create a pose-robust feature representation. Extensive experiments on two challenging benchmarks (CFP-FP and IJB-A) illustrate that our approach achieves a competitive performance in the field of pose-robust face recognition. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533665 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
DocType | ISSN | Citations |
Conference | 2161-4393 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenduo Zhang | 1 | 0 | 0.34 |
| Yongru Chen | 2 | 0 | 1.01 |
| WM | 3 | 221 | 34.28 |
| Guijin Wang | 4 | 405 | 49.34 |
| QM | 5 | 464 | 72.05 |