Name
Abstract | ||
|---|---|---|
Facial landmark localization plays an important role in face recognition and analysis applications. In this paper, we give a brief introduction to a coarse-to-fine pipeline with neural networks and sequential regression. First, a global convolutional network is applied to the holistic facial image to give an initial landmark prediction. A pyramid of multi-scale local image patches is then cropped to feed to a new network for each landmark to refine the prediction. As the refinement network outputs a more accurate position estimation than the input, such procedure could be repeated several times until the estimation converges. We evaluate our system on the 300-W dataset [11] and it outperforms the recent state-of-the-arts. |
Year | Venue | Field |
|---|---|---|
2015 | arXiv: Computer Vision and Pattern Recognition | Facial recognition system,Computer vision,Regression,Pattern recognition,Computer science,Pyramid,Artificial intelligence,Artificial neural network,Landmark,Machine learning |
DocType | Volume | Citations |
Journal | abs/1511.04901 | 2 |
PageRank | References | Authors |
0.35 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiao Huang | 1 | 9 | 3.13 |
| Erjin Zhou | 2 | 430 | 17.83 |
| Zhimin Cao | 3 | 521 | 22.27 |