Name
Title | ||
|---|---|---|
Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement |
Abstract | ||
|---|---|---|
Despite great advances witnessed on facial image alignment in recent years, high accuracy high speed face alignment algorithms still have rooms to improve especially for applications where computation resources are limited. Addressing this issue, we propose a new face landmark localization algorithm by combining global regression and local refinement. In particular, for a given image, our algorithm first estimates its global facial shape through a global regression network (GRegNet) and then using cascaded local refinement networks (LRefNet) to sequentially improve the alignment result. Compared with previous face alignment algorithms, our key innovation is the sharing of low level features in GRegNet with LRefNet. Such feature sharing not only significantly improves the algorithm efficiency, but also allows full exploration of rich locality-sensitive details carried with shallow network layers and consequently boosts the localization accuracy. The advantages of our algorithm is clearly validated in our thorough experiments on four popular face alignment benchmarks, 300-W, AFLW, COFW and WFLW. On all datasets, our algorithm produces state-of-the-art alignment accuracy, while enjoys the smallest computational complexity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPRW.2019.00036 | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | Field | DocType |
face alignment benchmarks,alignment accuracy,localization accuracy,shallow network layers,rich locality-sensitive details,algorithm efficiency,feature sharing,low level features,previous face alignment algorithms,alignment result,LRefNet,local refinement networks,GRegNet,global regression network,global facial shape,face landmark localization algorithm,computation resources,high accuracy high speed face alignment algorithms,facial image alignment | Computer vision,Pattern recognition,Regression,Computer science,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
2160-7508 | 978-1-7281-2507-7 | 1 |
PageRank | References | Authors |
0.34 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinzhan Su | 1 | 1 | 0.34 |
| Zhe Wang | 2 | 1 | 1.02 |
| Chunyuan Liao | 3 | 62 | 5.15 |
| Haibin Ling | 4 | 4531 | 215.76 |