Name
Abstract | ||
|---|---|---|
With the popularity of face recognition technology, people have put forward higher requirements for the security of face recognition system. Face anti-spoofing detection attracts extensive attention and many methods been proposed. However, these methods perform poorly in cross scenes. To solve this problem, we propose a face anti-spoofing detection algorithm based on domain adaptation. We apply Maximum Mean Discrepancy (MMD) to multi-layer network distribution adaptation, which improves the generalization ability of the model. To further improve the performance of face anti-spoofing detection, we fuse the low-level features with the high-level features of convolutional neural network for face anti-spoofing detection. Two widely used datasets are used to test the proposed method. The experimental results show that the proposed algorithm outperforms state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMEW.2019.00-88 | 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) |
Keywords | Field | DocType |
face anti-spoofing detection, deep learning, domain adaptation, Maximum Mean Discrepancy | Maximum mean discrepancy,Computer vision,Facial recognition system,Multi layer,Pattern recognition,Domain adaptation,Computer science,Convolutional neural network,Popularity,Artificial intelligence,Deep learning,Fuse (electrical) | Conference |
ISSN | ISBN | Citations |
2330-7927 | 978-1-5386-9215-8 | 1 |
PageRank | References | Authors |
0.35 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengshun Zhou | 1 | 1 | 0.35 |
| Chenqiang Gao | 2 | 23 | 8.86 |
| Fang Chen | 3 | 1 | 0.35 |
| Chaoyu Li | 4 | 1 | 0.35 |
| Xindou Li | 5 | 2 | 0.71 |
| Feng Yang | 6 | 6 | 1.79 |
| Yue Zhao | 7 | 1 | 2.38 |