Reproducible Research

The easiest way to reproduce the result presented in the paper is using the bash script rr.sh.

#!/usr/bin/env bash

function main(){
    install_package
    download_resources
    run_experiment_quality_based
    run_experiment_color_based
    retrieve_summary_results
}

function install_package(){
    python clean.py
    python bootstrap-buildout.py
    bin/buildout
}

function download_resources(){
    bin/download_resources.py -v
}

function run_experiment_quality_based(){
    bin/algorithmic_constrained_evaluation.py -r experiments/quality_based/configuration_msu_iqm_face_cropped.py
}

function run_experiment_color_based(){
    bin/algorithmic_constrained_evaluation.py -r experiments/color_based/configuration_boulkenafet_face_cropped.py
}

function retrieve_summary_results(){
    bin/create_summary_table.py -bp resources_bob_paper_icb2019_gradgpad/msuiqm_face_cropped/ -rp result/quality_based
    bin/create_summary_table.py -bp resources_bob_paper_icb2019_gradgpad/boulkenafet_face_cropped/ -rp result/color_based
}

main

This script downloads the extracted features and executes the experiments.

Once the rr.sh script is finished you may find the following files:

• Downloaded features will be stored in resources_bob_paper_icb2019_gradgpad

• Trained models will be stored in resources_bob_paper_icb2019_gradgpad

• Summary result tables will be stored in result

In this work we have used the framework presented in the chapter Challenges of Face Presentation Attack Detection in Real Scenarios in the Handbook of Biometric Anti-Spoofing. Take a look of the doc to better understand the framework.