Running baseline experiments in Cross database settings¶
This section explains how to run and evaluate the cross database experiments in OULU-NPU database and REPLAY-MOBILE database.
Note
For the experiments discussed in this section, the databases OULU-NPU and REPLAY-MOBILE needs to be downloaded and installed in your system. Please refer to Executing Baseline Algorithms section in the documentation of bob.pad.face package for more details on how to run the face PAD experiments and setup the databases.
Training on REPLAY-MOBILE and testing on OULU-Protocol_1¶
1. IQM-SVM train on REPLAY-MOBILE test on OULU-Protocol_1¶
The entire pipeline for IQM-SVM on grandtest of REPLAY-MOBILE database can be launched with the following command
bin/spoof.py \ # spoof.py is used to run the preprocessor
replay-mobile \ # run for Replay-Mobile database
iqm-svm \ # IQM-SVM configuration
--groups train dev eval \ # groups
--protocol grandtest \ # Protocol to use
--allow-missing-files \ # allow failed files
--grid idiap \ # use grid, only for Idiap users, REMOVE otherwise
--sub-directory <PATH_TO_STORE_IQM_RM_BASELINE_RESULTS> # define your path here
Now to test this trained model on OULU-Protocol; run the command below
bin/spoof.py \
oulunpu \
iqm-svm \
--groups train dev eval \
--protocol Protocol_1 \
--skip-projector-training \
--projector-file <PATH_TO_STORE_IQM_RM_BASELINE_RESULTS>/grandtest/Projector.hdf5 \
--allow-missing-files \
--grid idiap \
--sub-directory <PATH_TO_STORE_IQM_TRAIN_RM_TEST_OULU> -vv
2. LBP-SVM train on REPLAY-MOBILE test on OULU-Protocol_1¶
The entire pipeline for LBP-SVM on grandtest of REPLAY-MOBILE database can be launched with the following command
bin/spoof.py \ # spoof.py is used to run the preprocessor
replay-mobile \ # run for Replay-Mobile database
glbp-svm \ # LBP-SVM configuration
--groups train dev eval \ # groups
--protocol grandtest \ # Protocol to use
--allow-missing-files \ # allow failed files
--grid idiap \ # use grid, only for Idiap users, REMOVE otherwise
--sub-directory <PATH_TO_STORE_LBP_RM_BASELINE_RESULTS> # define your path here
Now to test this trained model on OULU-Protocol; run the command below
bin/spoof.py \
oulunpu \
qlbp-svm \
--groups train dev eval \
--protocol Protocol_1 \
--skip-projector-training \
--projector-file <PATH_TO_STORE_LBP_RM_BASELINE_RESULTS>/grandtest/Projector.hdf5 \
--allow-missing-files \
--grid idiap \
--sub-directory <PATH_TO_STORE_LBP_TRAIN_RM_TEST_OULU> -vv
3. Evaluating results of face PAD Experiments¶
The scores obtained can be evaluated with the following command.
For LBP baselines
bob pad metrics \
<PATH_TO_STORE_LBP_TRAIN_RM_TEST_OULU>/scores/scores-dev \
<PATH_TO_STORE_LBP_TRAIN_RM_TEST_OULU>/scores/scores-eval -e \
For IQM baselines
bob pad metrics \
<PATH_TO_STORE_IQM_TRAIN_RM_TEST_OULU>/scores/scores-dev \
<PATH_TO_STORE_IQM_TRAIN_RM_TEST_OULU>/scores/scores-eval -e \
Training on OULU-Protocol_1 and and testing on REPLAY-MOBILE¶
1. IQM-SVM train on OULU-Protocol_1 and and testing on REPLAY-MOBILE¶
The entire pipeline for IQM-SVM on Protocol_1 of OULU database can be launched with the following command
bin/spoof.py \ # spoof.py is used to run the preprocessor
oulunpu \ # run for Replay-Mobile database
iqm-svm \ # IQM-SVM configuration
--groups train dev eval \ # groups
--protocol Protocol_1 \ # Protocol to use
--allow-missing-files \ # allow failed files
--grid idiap \ # use grid, only for Idiap users, REMOVE otherwise
--sub-directory <PATH_TO_STORE_IQM_OULU_BASELINE_RESULTS> # define your path here
Now to test this trained model on OULU-Protocol; run the command below
bin/spoof.py \
replay-mobile \
iqm-svm \
--groups train dev eval \
--protocol grandtest \
--skip-projector-training \
--projector-file <PATH_TO_STORE_IQM_OULU_BASELINE_RESULTS>/Protocol_1/Projector.hdf5 \
--allow-missing-files \
--grid idiap \
--sub-directory <PATH_TO_STORE_IQM_TRAIN_OULU_TEST_RM> -vv
2. LBP-SVM train on OULU-Protocol_1 and and testing on REPLAY-MOBILE¶
The entire pipeline for LBP-SVM on Protocol_1 of OULU database can be launched with the following command
bin/spoof.py \ # spoof.py is used to run the preprocessor
oulunpu \ # run for Replay-Mobile database
glbp-svm \ # LBP-SVM configuration
--groups train dev eval \ # groups
--protocol Protocol_1 \ # Protocol to use
--allow-missing-files \ # allow failed files
--grid idiap \ # use grid, only for Idiap users, REMOVE otherwise
--sub-directory <PATH_TO_STORE_LBP_OULU_BASELINE_RESULTS> # define your path here
Now to test this trained model on OULU-Protocol; run the command below
bin/spoof.py \
replay-mobile \
glbp-svm \
--groups train dev eval \
--protocol grandtest \
--skip-projector-training \
--projector-file <PATH_TO_STORE_LBP_OULU_BASELINE_RESULTS>/Protocol_1/Projector.hdf5 \
--allow-missing-files \
--grid idiap \
--sub-directory <PATH_TO_STORE_LBP_TRAIN_OULU_TEST_RM> -vv
3. Evaluating results of face PAD Experiments¶
The scores obtained can be evaluated with the following command.
For LBP baselines
bob pad metrics \
<PATH_TO_STORE_LBP_TRAIN_OULU_TEST_RM>/scores/scores-dev \
<PATH_TO_STORE_LBP_TRAIN_OULU_TEST_RM>/scores/scores-eval -e \
For IQM baselines
bob pad metrics \
<PATH_TO_STORE_IQM_TRAIN_OULU_TEST_RM>/scores/scores-dev \
<PATH_TO_STORE_IQM_TRAIN_OULU_TEST_RM>/scores/scores-eval -e \