CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices.
![image](https://raw.githubusercontent.com/bluelml/CenterFace/master/results/bl4.jpg)
2019.09.13
CenterFace is released.
- OpenCV 4.1.0
- Numpy
- Python3.6+
- Results on val set of WIDER FACE:
Model Version |
Easy Set |
Medium Set |
Hard Set |
FaceBoxes |
0.840 |
0.766 |
0.395 |
FaceBoxes3.2× |
0.798 |
0.802 |
0.715 |
RetinaFace-mnet |
0.896 |
0.871 |
0.681 |
LFFD-v1 |
0.910 |
0.881 |
0.780 |
LFFD-v2 |
0.837 |
0.835 |
0.729 |
CenterFace |
0.935 |
0.924 |
0.875 |
CenterFace-small |
0.931 |
0.924 |
0.870 |
- Results on test set of WIDER FACE:
Model Version |
Easy Set |
Medium Set |
Hard Set |
FaceBoxes |
0.839 |
0.763 |
0.396 |
FaceBoxes3.2× |
0.791 |
0.794 |
0.715 |
RetinaFace-mnet |
0.896 |
0.871 |
0.681 |
LFFD-v1 |
0.910 |
0.881 |
0.780 |
LFFD-v2 |
0.837 |
0.835 |
0.729 |
CenterFace |
0.932 |
0.921 |
0.873 |
- RetinaFace-mnet is short for RetinaFace-MobileNet-0.25 from excellent work insightface.
- LFFD-v1 is from prefect work LFFD.
- CenterFace/CenterFace-small evaluation is under MULTI-SCALE, FLIP.
- For SIO(Single Inference on the Original) evaluation schema, CenterFace also produces 92.2% (Easy), 91.1% (Medium) and 78.2% (Hard) for validation set.
Model Version |
Disc ROC curves score |
RetinaFace-mnet |
96.0@1000 |
LFFD-v1 |
97.3@1000 |
LFFD-v2 |
97.2@1000 |
CenterFace |
97.9@1000 |
CenterFace-small |
98.1@1000 |
- Latency on NVIDIA RTX 2080TI:
Resolution-> |
640×480 |
1280×720(704) |
1920×1080(1056) |
RetinaFace-mnet |
5.40ms |
6.31ms |
10.26ms |
LFFD-v1 |
7.24ms |
14.58ms |
28.36ms |
CenterFace |
5.5ms |
6.4ms |
8.7ms |
CenterFace-small |
4.4ms |
5.7ms |
7.3ms |
![image](https://raw.githubusercontent.com/bluelml/CenterFace/master/results/box_lm.jpg)
![image](https://raw.githubusercontent.com/bluelml/CenterFace/master/results/bl3.jpg)
![image](https://raw.githubusercontent.com/bluelml/CenterFace/master/results/bl1.jpg)
Welcome to join in QQ Group(229042802) for more discussion, including but not limited to face detection, face anti-spoofing and so on.
If you benefit from our work in your research and product, please consider to cite the following related papers:
@inproceedings{CenterFace,
title={CenterFace: Joint Face Detection and Alignment Using Face as Point},
author={Xu, Yuanyuan and Yan, Wan and Sun, Haixin and Yang, Genke and Luo, Jiliang},
booktitle={arXiv:1911.03599},
year={2019}
}