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https://github.com/jwyang/person-specific-fas

Codes for TIFS paper
https://github.com/jwyang/person-specific-fas

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Codes for TIFS paper

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README 

        
# Person-Specific-Face-Anti-Spoofing

## Introduction



This repo provides the code for our TIFS paper "[Person Specific Face Anti-Spoofing with Subject Domain Adaptation](http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7041231&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7041231)".

Different from previous works, we proposed to perform face anti-spoofing in person-specific manner, which means each subject

is equipped with a unique classifier for distinguishing genuine and fake face images. Moreover, we considered a challenging case that 

some subjects' fake face images are not available. Toward such a problem, we proposed a subject domain adaptation algorithm to 

generate virtual samples for training. The experimental results proves the superiority of person-specific face anti-spoofing method compared

with generic face anti-spoofing methods. Also, it is shown the proposed subject domain adaptation algorithm can generate virtual samples to 

assist the training of person-specific classifiers.



## Experiments



### Preparation

* libsvm: this open library can be found [here](http://www.csie.ntu.edu.tw/~cjlin/libsvm/).

* features: the off-the-shelf features extracted for this paper can be downloaded from my Baidu Driver [here](http://pan.baidu.com/s/1hqKmEpe) or Google Driver [here](https://drive.google.com/folderview?id=0B749j8XpVZQ-VFE0OG1hZFpFZXc&usp=sharing)



### Run the code



Use Replay-Attack dataset as the example. Go to REPLAY-ATTACK folder, and then:



1. Load Feats and Labels.



   We need load all features and labels for Replay-Attack dataset. Run

   ```bash

   load('Feats_Replay-Attack_tr_fall_soriginal.mat')  % load train set features

   load('Labels_Replay-Attack_tr_fall_soriginal.mat') % load train set labels

   load('Feats_Replay-Attack_dev_fall_soriginal.mat') % load development set features

   load('Labels_Replay-Attack_dev_fall_soriginal.mat') % load development set labels

   load('Feats_Replay-Attack_te_fall_soriginal.mat') % load test set features

   load('Labels_Replay-Attack_te_fall_soriginal.mat') % load test set labels

   load('Feats_Replay-Attack_en_fall_soriginal.mat') % load enrollment set features

   load('Labels_Replay-Attack_en_fall_soriginal.mat') % load enrollment set labels

   ```



2. Estimate Transformation.



    Go to folder Estimate_Transformationn_for_Expe, fund runall.m:

    ```bash

    Feat_Types = [1 3];

    BLabels = {'ContN' 'AdvN'};



    for t = 1:length(Feat_Types)

        for b = 1:length(BLabels)

            EstimateTransformation_Enroll_Iterative(Feats_enroll_SL, Labels_train_SL, Labels_devel_SL, Labels_test_SL, Labels_enroll_SL, BLabels{b}, Feat_Types(t), 'CS');

            EstimateTransformation_Enroll_Iterative(Feats_enroll_SL, Labels_train_SL, Labels_devel_SL, Labels_test_SL, Labels_enroll_SL, BLabels{b}, Feat_Types(t), 'OLS');

            EstimateTransformation_Enroll_Iterative(Feats_enroll_SL, Labels_train_SL, Labels_devel_SL, Labels_test_SL, Labels_enroll_SL, BLabels{b}, Feat_Types(t), 'PLS');

            EstimateTransformation_Enroll_PCA(Feats_enroll_SL, Labels_train_SL, Labels_devel_SL, Labels_test_SL, Labels_enroll_SL, Feat_Types(t), BLabels{b}, 'PCA');  

            % EstimateTransformation_Enroll_PCA_withSubNum(Feats_enroll_SL, Labels_train_SL, Labels_devel_SL, Labels_test_SL, Labels_enroll_SL, Feat_Types(t), BLabels{b}, 'PCA');

        end

    end	

	```



	In this file, we provided all methods to estimate the transformations, including CS, OLS, PLS as stated in the paper. We also provided a PCA-based transformation estimation. To get the transformation, comment the line as you need. Once finished, you will obtain a mat file with prefix "transform_".

	

3. Domain Adaptation.

    After get the estimated transformation, we can perform domain adaptation between subjects. Go to Domain_Adaptation folder, and find runall.m:

	```bash

	Feat_Types = [1 3];

    Methods = {'CS' 'OLS' 'PLS' 'PCA'};



    for t = 1:2

        for m = 1:4 % length(Methods)

            TargetDA_AllQualities(Feats_train_SL, Labels_train_SL, Feats_devel_SL, Labels_devel_SL, Feats_test_SL, Labels_test_SL, Feats_enroll_SL, Labels_enroll_SL, Feat_Types(t), Methods{m})        

        end

    end

	```

    Modify the variable *Methods* accordingly to do domain adaptation for different methods. Afterward, you will find mat files with prefix "SynthFeatures_".

	

4. Train Model.



    Congratulate to see you here. You are almost set! Go inside Train_Models folder. You can train generic face anti-spoofing model using runall_gen.m, while person-specific face anti-spoofing model using runall_ps.m. Meanwhile, you can also use runall.m to train models with the number of subjects used for training.

	

5. Test Model

	

	Finally, you may want to evaluate the performance. Go to Test_Models folder, find runall_ps.m and runall_gen.m and run them.

 

## Citation

If you find our paper or code is useful in your researches, please consider citing:



    @article{yang2015person,

      title={Person-specific face antispoofing with subject domain adaptation},

      author={Yang, Jianwei and Lei, Zhen and Yi, Dong and Li, Stan Z},

      journal={IEEE Transactions on Information Forensics and Security},

      volume={10},

      number={4},

      pages={797--809},

      year={2015},

      publisher={IEEE}

    }