- This is a single shot face anti-spoofing project.
- The deep learning framework is Pytorch. Python3.5 is used.
- sudo -s
- sh install_requirements.sh
- exit
- face_alignment is used for landmarks extraction. Page face_alignment. Thanks to them.
- cd detlandmark&&python3 detlandmark_imgs.py NUAA_raw_dir
-
I have upload data and detected landmarks into GOOGLE DRIVE-raw.tar.gz
-
I have upload data and detected landmarks into Baidu DRIVE-raw.tar.gz
-
You can change corresponding directory and filename in config.py
-
For example train_filelists=[ ['raw/ClientRaw','raw/client_train_raw.txt',GENUINE], ['raw/ImposterRaw','imposter_train_raw.txt',ATTACK] ] test_filelists=[ ['raw/ClientRaw','raw/client_test_raw.txt',GENUINE], ['raw/ImposterRaw','raw/imposter_test_raw.txt',ATTACK] ]
- Our method is straightforward. Small patched containing a face is cropped with corresponding landmarks. A binary classification network is used to distinguish the attack patches.
- First, edit file config.py, choose the target network and proper batch_size.
- Then, in terminal command:
make clean&&make&&python3 main.py train
- In terminal command:
python3 inference.py inference --images='detlandmark/inference_images/*/*.jpg' - The inference report is result/inference.txt, you can check it in commad:
cat result/inference.txt
- We have fixed the bug of choice wrong face in multiple detected faces with standard of coordinates.
- To visualize cropped faces in dataset. Please run command:
python3 vis_cropface.py visualize - All faces will be shown in data/showcropface_train.jpg and data/showcropface_val.jpg
- The training data are shown here. Training Validation
- Experiments results on NUAA[1] Image input size is as same as the imagenet.
- State-of-the-art networks are used, e.g. VGG[2], ResNet[3], DenseNet[4], Inception[5], Xception[6], DetNet[7]
| Network | Acc | AUC | EER | TPR(1.0%) | TPR(.5%) |
|---|---|---|---|---|---|
| VGG-11 | 0.9416 | 0.99600562 | 0.031592 | 0.948099 | 0.931262 |
| VGG-13 | 0.9452 | 0.99261419 | 0.034890 | 0.908696 | 0.869814 |
| VGG-16 | 0.9591 | 0.99449404 | 0.027599 | 0.952283 | 0.926575 |
| VGG-19 | 0.9013 | 0.99623176 | 0.023086 | 0.958378 | 0.941503 |
| Res-18 | 0.9813 | 0.99872778 | 0.008158 | 0.992470 | 0.989585 |
| Res-34 | 0.9656 | 0.99978646 | 0.003992 | 0.998091 | 0.996181 |
| Res-50 | 0.8677 | 0.99951550 | 0.008923 | 0.991668 | 0.986544 |
| denseNet121 | 0.9803 | 0.99872628 | 0.014754 | 0.981534 | 0.970144 |
| denseNet161 | 0.9757 | 0.99610439 | 0.016664 | 0.977222 | 0.967020 |
| denseNet169 | 0.9334 | 0.99532942 | 0.029744 | 0.949662 | 0.932130 |
| denseNet201 | 0.9263 | 0.99833348 | 0.012195 | 0.985718 | 0.975525 |
| InceptionV3 | 0.8078 | 0.99172718 | 0.036278 | 0.927270 | 0.907655 |
| Xception | 0.9843 | 0.99973281 | 0.005728 | 0.996431 | 0.993101 |
| DetNet | 0.9072 | 0.99998322 | 0.000892 | 0.999705 | 0.999703 |
- [1]Tan X, Li Y, Liu J, et al. Face liveness detection from a single image with sparse low rank bilinear discriminative model[C]// European Conference on Computer Vision. Springer-Verlag, 2010:504-517.
- [2]Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
- [3]He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
- [4]Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//CVPR. 2017, 1(2): 3.
- [5]Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2818-2826.
- [6]Chollet F. Xception: Deep learning with depthwise separable convolutions[J]. arXiv preprint, 2017: 1610.02357.
- [7]Li Z, Peng C, Yu G, et al. DetNet: A Backbone network for Object Detection[J]. arXiv preprint arXiv:1804.06215, 2018.
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