SPIGA: Shape Preserving Facial Landmarks with Graph Attention Networks.
This repository contains the models weights of SPIGA, a face alignment and headpose estimator that takes advantage of the complementary benefits from CNN and GNN architectures producing plausible face shapes in presence of strong appearance changes.
Setup
The repository is available on github
Results
WFLW Dataset
 |
NME_ioc |
AUC_10 |
FR_10 |
NME_P90 |
NME_P95 |
NME_P99 |
| full |
4.060 |
60.558 |
2.080 |
6.766 |
8.199 |
13.071 |
| pose |
7.141 |
35.312 |
11.656 |
10.684 |
13.334 |
26.890 |
| expression |
4.457 |
57.968 |
2.229 |
7.023 |
8.148 |
22.388 |
| illumination |
4.004 |
61.311 |
1.576 |
6.528 |
7.919 |
11.090 |
| makeup |
3.809 |
62.237 |
1.456 |
6.320 |
8.289 |
11.564 |
| occlusion |
4.952 |
53.310 |
4.484 |
8.091 |
9.929 |
16.439 |
| blur |
4.650 |
55.310 |
2.199 |
7.311 |
8.693 |
14.421 |
MERLRAV Dataset
 |
NME_bbox |
AUC_7 |
FR_7 |
NME_P90 |
NME_P95 |
NME_P99 |
| full |
1.509 |
78.474 |
0.052 |
2.163 |
2.468 |
3.456 |
| frontal |
1.616 |
76.964 |
0.091 |
2.246 |
2.572 |
3.621 |
| half_profile |
1.683 |
75.966 |
0.000 |
2.274 |
2.547 |
3.397 |
| profile |
1.191 |
82.990 |
0.000 |
1.735 |
2.042 |
2.878 |
300W Private Dataset
 |
NME_bbox |
AUC_7 |
FR_7 |
NME_P90 |
NME_P95 |
NME_P99 |
| full |
2.031 |
71.011 |
0.167 |
2.788 |
3.078 |
3.838 |
| indoor |
2.035 |
70.959 |
0.333 |
2.726 |
3.007 |
3.712 |
| outdoor |
2.027 |
37.174 |
0.000 |
2.824 |
3.217 |
3.838 |
COFW68 Dataset
 |
NME_bbox |
AUC_7 |
FR_7 |
NME_P90 |
NME_P95 |
NME_P99 |
| full |
2.517 |
64.050 |
0.000 |
3.439 |
4.066 |
5.558 |
300W Public Dataset
 |
NME_ioc |
AUC_8 |
FR_8 |
NME_P90 |
NME_P95 |
NME_P99 |
| full |
2.994 |
62.726 |
0.726 |
4.667 |
5.436 |
7.320 |
| common |
2.587 |
44.201 |
0.000 |
3.710 |
4.083 |
5.215 |
| challenge |
4.662 |
42.449 |
3.704 |
6.626 |
7.390 |
10.095 |
BibTeX Citation
@inproceedings{Prados-Torreblanca_2022_BMVC,
author = {Andrés Prados-Torreblanca and José M Buenaposada and Luis Baumela},
title = {Shape Preserving Facial Landmarks with Graph Attention Networks},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0155.pdf}
}
