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spiga
aprados/spiga
spiga is a machine learning model from aprados. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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 presen…
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Updated May 20, 2024
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At a glance
- Access
- Public
- Created
- May 20, 2024
- Updated
- May 20, 2024
- SHA
- eae17077
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From the Hugging Face model README
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.
<p align="center"> <img src="https://raw.githubusercontent.com/andresprados/SPIGA/main/assets/spiga_scheme.png" width="80%"> </p>Setup
The repository is available on github
Results
<details> <summary> WFLW Dataset </summary> | 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 |
 | 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 |
 | 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 |
 | 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 |
 | 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}
}
- Created
- May 20, 2024
- Updated
- May 20, 2024