Executing Other Algorithms¶
This section explains how to execute face presentation attack detection (PAD) algorithms implemented
in bob.pad.face.
Warning
Algorithms introduced in this section might be in the process of publishing. Therefore, it is not
allowed to publish results introduced in this section without permission of the owner of the package.
If you are planning to use the results from this section, please contact the owner of the package first.
Please check the setup.py for contact information.
Running face PAD Experiments¶
To run the PAD experiments, the spoof.py script located in bin directory is used.
To see the description of the script you can type in the console:
$ spoof.py --help
This script is explained in more detail in Running Presentation Attack Detection Experiments.
Usually it is a good idea to have at least verbose level 2 (i.e., calling
spoof.py --verbose --verbose, or the short version spoof.py
-vv).
Note
Running in Parallel
To run the experiments in parallel, you can define an SGE grid or local host (multi-processing) configurations as explained in Running in Parallel.
In short, to run in the Idiap SGE grid, you can simply add the --grid
command line option, with grid configuration parameters. To run experiments in parallel on
the local machine, simply add a --parallel <N> option, where <N>
specifies the number of parallel jobs you want to execute.
Database setups and face PAD algorithms are encoded using
Configuration Files, all stored inside the package root, in
the directory bob/pad/face/config. Documentation for each resource
is available on the section Resources.
Warning
You cannot run experiments just by executing the command line instructions described in this guide. You need first to procure yourself the raw data files that correspond to each database used here in order to correctly run experiments with those data. Biometric data is considered private date and, under EU regulations, cannot be distributed without a consent or license. You may consult our Databases resources section for checking currently supported databases and accessing download links for the raw data files.
Once the raw data files have been downloaded, particular attention should be given to the directory locations of those. Unpack the databases carefully and annotate the root directory where they have been unpacked.
Then, carefully read the Databases section of
Installation Instructions on how to correctly setup the
~/.bob_bio_databases.txt file.
Use the following keywords on the left side of the assignment (see Databases):
[YOUR_REPLAY_ATTACK_DIRECTORY] = /complete/path/to/replayattack-database/
Notice it is rather important to use the strings as described above,
otherwise bob.pad.base will not be able to correctly load your images.
Once this step is done, you can proceed with the instructions below.
Anomaly detection based PAD on Aggregated Database¶
This section summarizes the results of anomaly detection based face PAD experiments on the Aggregated Database.
The description of the database-related settings, which are used to run face PAD algorithms on the Aggregated Db is given here Aggregated Database. To understand the settings in more details you can check the corresponding configuration file : bob/pad/face/config/aggregated_db.py.
Results for grandtest protocol¶
This section summarizes the evaluation results on the grandtest protocol of the Aggregated database for the following face PAD algorithms (for more details click on the corresponding algorithm):
- Image Quality Measures as features of facial region + GMM-based one-class classifier (anomaly detector),
- Image Quality Measures as features of facial region + one-class SVM classifier (anomaly detector) for Aggregated Database,
- Image Quality Measures as features of facial region + Logistic Regression,
- Image Quality Measures as features of facial region + SVM for Aggregated Database.
For a more detailed understanding of above pipe-lines you can also check corresponding configuration files:
bob/pad/face/config/qm_one_class_gmm.py,bob/pad/face/config/qm_one_class_svm_aggregated_db.py,bob/pad/face/config/qm_lr.py,bob/pad/face/config/qm_svm_aggregated_db.py.
To run above algorithms on the Aggregated Database database, using the grandtest protocol, execute the following:
$ spoof.py aggregated-db qm-one-class-gmm \
--sub-directory <PATH_TO_STORE_THE_RESULTS_1>
$ spoof.py aggregated-db qm-one-class-svm-aggregated-db \
--sub-directory <PATH_TO_STORE_THE_RESULTS_2>
$ spoof.py aggregated-db qm-lr \
--sub-directory <PATH_TO_STORE_THE_RESULTS_3>
$ spoof.py aggregated-db qm-svm-aggregated-db \
--sub-directory <PATH_TO_STORE_THE_RESULTS_4>
Tip
If you are in idiap you can use SGE grid to speed-up the calculations.
Simply add --grid idiap argument to the above command. For example:
To evaluate the results computing EER, HTER and plotting ROC you can use the following command:
./bin/evaluate.py \
--dev-files \
<PATH_TO_STORE_THE_RESULTS_1>/grandtest/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_2>/grandtest/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_3>/grandtest/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_4>/grandtest/scores/scores-dev \
--eval-files \
<PATH_TO_STORE_THE_RESULTS_1>/grandtest/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_2>/grandtest/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_3>/grandtest/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_4>/grandtest/scores/scores-eval \
--legends \
"IQM + one-class GMM + Aggregated Db" \
"IQM + one-class SVM + Aggregated Db" \
"IQM + two-class LR + Aggregated Db" \
"IQM + two-class SVM + Aggregated Db" \
-F 7 \
--criterion EER \
--roc <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
The EER/HTER errors for the Aggregated Database database are summarized in the Table below:
| Algorithm | EER,% | HTER,% |
|---|---|---|
| IQM + one-class GMM | 19.336 | 20.769 |
| IQM + one-class SVM | 28.137 | 34.776 |
| IQM + two-class LR | 10.354 | 11.856 |
| IQM + two-class SVM | 12.710 | 15.253 |
The ROC curves for the particular experiment can be downloaded from here:
Results for photo-photo-video protocol¶
This section summarizes the evaluation results on the photo-photo-video protocol of the Aggregated database for the following face PAD algorithms (for more details click on the corresponding algorithm):
- Image Quality Measures as features of facial region + GMM-based one-class classifier (anomaly detector),
- Image Quality Measures as features of facial region + one-class SVM classifier (anomaly detector) for Aggregated Database,
- Image Quality Measures as features of facial region + Logistic Regression,
- Image Quality Measures as features of facial region + SVM for Aggregated Database.
For a more detailed understanding of above pipe-lines you can also check corresponding configuration files:
bob/pad/face/config/qm_one_class_gmm.py,bob/pad/face/config/qm_one_class_svm_aggregated_db.py,bob/pad/face/config/qm_lr.py,bob/pad/face/config/qm_svm_aggregated_db.py.
To run above algorithms on the Aggregated Database database, using the photo-photo-video protocol, execute the following:
$ spoof.py aggregated-db qm-one-class-gmm \
--protocol photo-photo-video \
--sub-directory <PATH_TO_STORE_THE_RESULTS_1>
$ spoof.py aggregated-db qm-one-class-svm-aggregated-db \
--protocol photo-photo-video \
--sub-directory <PATH_TO_STORE_THE_RESULTS_2>
$ spoof.py aggregated-db qm-lr \
--protocol photo-photo-video \
--sub-directory <PATH_TO_STORE_THE_RESULTS_3>
$ spoof.py aggregated-db qm-svm-aggregated-db \
--protocol photo-photo-video \
--sub-directory <PATH_TO_STORE_THE_RESULTS_4>
Tip
If you are in idiap you can use SGE grid to speed-up the calculations.
Simply add --grid idiap argument to the above command. For example:
To evaluate the results computing EER, HTER and plotting ROC you can use the following command:
./bin/evaluate.py \
--dev-files \
<PATH_TO_STORE_THE_RESULTS_1>/photo-photo-video/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_2>/photo-photo-video/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_3>/photo-photo-video/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_4>/photo-photo-video/scores/scores-dev \
--eval-files \
<PATH_TO_STORE_THE_RESULTS_1>/photo-photo-video/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_2>/photo-photo-video/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_3>/photo-photo-video/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_4>/photo-photo-video/scores/scores-eval \
--legends \
"IQM + one-class GMM + Aggregated Db" \
"IQM + one-class SVM + Aggregated Db" \
"IQM + two-class LR + Aggregated Db" \
"IQM + two-class SVM + Aggregated Db" \
-F 7 \
--criterion EER \
--roc <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
The EER/HTER errors for the Aggregated Database database are summarized in the Table below:
| Algorithm | EER,% | HTER,% |
|---|---|---|
| IQM + one-class GMM | 22.075 | 14.470 |
| IQM + one-class SVM | 35.537 | 24.317 |
| IQM + two-class LR | 10.184 | 30.132 |
| IQM + two-class SVM | 10.527 | 21.926 |
The ROC curves for the particular experiment can be downloaded from here:
Results for video-video-photo protocol¶
This section summarizes the evaluation results on the video-video-photo protocol of the Aggregated database for the following face PAD algorithms (for more details click on the corresponding algorithm):
- Image Quality Measures as features of facial region + GMM-based one-class classifier (anomaly detector),
- Image Quality Measures as features of facial region + one-class SVM classifier (anomaly detector) for Aggregated Database,
- Image Quality Measures as features of facial region + Logistic Regression,
- Image Quality Measures as features of facial region + SVM for Aggregated Database.
For a more detailed understanding of above pipe-lines you can also check corresponding configuration files:
bob/pad/face/config/qm_one_class_gmm.py,bob/pad/face/config/qm_one_class_svm_aggregated_db.py,bob/pad/face/config/qm_lr.py,bob/pad/face/config/qm_svm_aggregated_db.py.
To run above algorithms on the Aggregated Database database, using the video-video-photo protocol, execute the following:
$ spoof.py aggregated-db qm-one-class-gmm \
--protocol video-video-photo \
--sub-directory <PATH_TO_STORE_THE_RESULTS_1>
$ spoof.py aggregated-db qm-one-class-svm-aggregated-db \
--protocol video-video-photo \
--sub-directory <PATH_TO_STORE_THE_RESULTS_2>
$ spoof.py aggregated-db qm-lr \
--protocol video-video-photo \
--sub-directory <PATH_TO_STORE_THE_RESULTS_3>
$ spoof.py aggregated-db qm-svm-aggregated-db \
--protocol video-video-photo \
--sub-directory <PATH_TO_STORE_THE_RESULTS_4>
Tip
If you are in idiap you can use SGE grid to speed-up the calculations.
Simply add --grid idiap argument to the above command. For example:
To evaluate the results computing EER, HTER and plotting ROC you can use the following command:
./bin/evaluate.py \
--dev-files \
<PATH_TO_STORE_THE_RESULTS_1>/video-video-photo/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_2>/video-video-photo/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_3>/video-video-photo/scores/scores-dev \
<PATH_TO_STORE_THE_RESULTS_4>/video-video-photo/scores/scores-dev \
--eval-files \
<PATH_TO_STORE_THE_RESULTS_1>/video-video-photo/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_2>/video-video-photo/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_3>/video-video-photo/scores/scores-eval \
<PATH_TO_STORE_THE_RESULTS_4>/video-video-photo/scores/scores-eval \
--legends \
"IQM + one-class GMM + Aggregated Db" \
"IQM + one-class SVM + Aggregated Db" \
"IQM + two-class LR + Aggregated Db" \
"IQM + two-class SVM + Aggregated Db" \
-F 7 \
--criterion EER \
--roc <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
The EER/HTER errors for the Aggregated Database database are summarized in the Table below:
| Algorithm | EER,% | HTER,% |
|---|---|---|
| IQM + one-class GMM | 13.503 | 29.794 |
| IQM + one-class SVM | 18.234 | 39.502 |
| IQM + two-class LR | 1.499 | 30.268 |
| IQM + two-class SVM | 1.422 | 24.901 |
The ROC curves for the particular experiment can be downloaded from here: