Name
Abstract | ||
|---|---|---|
A few well-developed face recognition pipelines have been reported in recent years. Most of the face-related work focuses on a specific module or demonstrates a research idea. In this paper, we present a pose-invariant 3D-aided 2D face recognition system (3D2D-PIFR) that is robust to pose variations as large as 90° by leveraging deep learning technology. We describe the architecture and the interface of 3D2D-PIFR, and introduce each module in detail. Experiments are conducted on the UHDB31 and IJB-A, demonstrating that 3D2D-PIFR outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software (COTS) by at least 9% on UHDB31 and 3% on IJB-A dataset on average. It fills a gap by providing a 3D-aided 2D face recognition system that has compatible results with 2D face recognition systems using deep learning techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BTAS.2017.8272729 | 2017 IEEE International Joint Conference on Biometrics (IJCB) |
Keywords | Field | DocType |
3D-aided pose invariant 2D face recognition system,deep learning technology,3D2D-PIFR | Facial recognition system,Architecture,Economics,Feature extraction,Software,Invariant (mathematics),Solid modeling,Artificial intelligence,Deep learning,Finance,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-1125-8 | 4 | 0.37 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Xu | 1 | 30 | 5.58 |
| Le An Ha | 2 | 109 | 15.68 |
| Pengfei Dou | 3 | 14 | 3.17 |
| Yuhang Wu | 4 | 21 | 6.11 |
| Ioannis A. Kakadiaris | 5 | 1910 | 203.66 |