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Pre-requisites and Setting-up the experiments 
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Downloading the dataset
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The dataset **XCSMAD** used in this study should be downloaded from Idiap's server.
We have provided the preprocessed data stored in the HDF format.

Alternatively, if you require the access to original (unprocessed) data, you should download the **WMCA** dataset from Idiap's server.

Upon downloading, you need to set the path to the database in the configuration file. Bob supports a configuration file (``~/.bob_bio_databases.txt``) in your home directory to specify where the
databases are located. Please specify the paths for the database like below (by editing the file manually)::

     $ cat ~/.bob_bio_databases.txt
     [XCSMAD_DIRECTORY] = <path-of-dataset-location>

If you download the **WMCA** dataset, you will also require the metadata used by the same.


Downloading the face recognition CNN model
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Pre-trained face recognition (FR) model of ``LightCNN-9`` can be downloaded from here_, or its own website.
The location of this model should be stored in ``.bobrc`` file in your $HOME directory in a json (key:value) format as follows::

    {
    "LIGHTCNN9_MODEL_DIRECTORY": <path-of-the-directory>
    }

.. _here: https://github.com/AlfredXiangWu/LightCNN

Only the directory should be specified. Do *not* include the model name. 


Setting up annotations directory
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You should specify the annotation directory for XCSMAD in configuration file (``~/.bob_bio_databases.txt``). 
If the annotations are pre-computed, provide the same path. Otherwise, the annotations will be computed for
the first time, and these will be re-used later. Please specify the paths for the database like below (by editing the file manually)::

     $ cat ~/.bob_bio_databases.txt
     [XCSMAD_ANNOTATION_DIRECTORY] = <path-of-dataset-location>


Generating set of commands 
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The complete setup runs 20 experiments on individual single channels (4 channels *X* 5 experiments); 3 experiments of feature fusion and score fusion, each; multiple cross-validation experiments; and
vulnerability analysis. To facilitate quick running and evaluation of experiments, a simple script is provided to programmatically generate all commands.  
You can specify the `base directory` where all the results should be stored, and few other parameters in ``config.ini`` in the present folder. Run the python script ``generate_commands.py`` in this folder. 
As a result, a new text file ``commands.txt`` will be generated in the same folder which consists of necessary commands. The commands are divided into 5 sections: (1) single channel PAD, 
(2) feature fusion, (3) score fusion, (4) cross-validation (for CNN+LR method on VIS data), and (5) vulnerability analysis.