shicaiwei123/patch_based_cnn

the implement of Face Anti-Spoofing Using Patch and Depth-Based CNNs

Python

patch_based_cnn

the implement of Face Anti-Spoofing Using Patch and Depth-Based CNNs

English|中文

Introduction

data
- The data after dividing into different patchs.
lib
- Shared basic functions: dataset, data processing and model training
model
- the code of net
output
- save model and logs
src
- main function and other auxiliary functions
test
- test a single image, which is used for deployment
config.py
- parameter configuration

User guide

patch_based_cnn
- Firstly, run generate.py to divide the living and spoofing img into different patches according to paper and save them as the test set and training set in the data folder. In this section, users need to prepare their own data or public data sets. We did not upload the data. You need to modify the path to your own data path.
- Modify the configuration file: patch_based_cnn.py
- Then train and test.
Run
- Divide img to different patchs(64 is used in article)
```
python3 data_generate.py
```
- Train and test
```
cd src
python3 patch_cnn_main.py
```
- Single image test. In this section,you can test a single image,which is used for deployment.
```
cd test 
python patch_cnn_test.py
```
Result
- the data enhancement such as learning rate decay and mixup has not been used for the follow results.
  
  Dataset Average accuracy(%)
  
  CASIA-FASD 93.52
  
  CASIA-SURF 88.88
depth_based_cnn
- Firstly, run PRNET to get depth map of living faces. We did not upload the data. You need to modify the path to your own data path. it is worth noting that acquiring depth maps from PRNET may take much time, so we take a 52x52 matrix with 1 as the ground truth of living faces here.
- Modify the configuration file: patch_based_cnn.py
- Then train and test.
Run
- Train and test
```
cd src
python3 depth_cnn_main.py
```
- Single image test. In this section,you can test a single image,which is used for deployment.
```
cd test 
python depth_cnn_test.py
```