WIP: Config for complete PAD experiment

This MR contains a config to run a complete multi-channel face PAD experiment using autoencoders + MLP based system, and a corresponding documentation.

TODO: finalize an MLP based algorithm in the config

TODO: doc on how to run a PAD experiment, once autoencoder and MLP training is completed.

doc/mc_autoencoder_pad.rst

@@ -105,3 +105,26 @@ To prepare the training data one can use the following command:
 Once above script is completed, the MC data suitable for autoencoder fine-tuning is located in the folder ``<PATH_TO_STORE_THE_RESULTS>/preprocessed/``.
 Now the autoencoder can be fine-tuned. Again, the fine-tuning procedure is explained in the **Convolutional autoencoder** section in the documentation of the ``bob.learn.pytorch`` package.
 
+
+3. Train an MLP using multi-channel autoencoder latent embeddings from WMCA
+=================================================================================
+
+Once auto-encoders are pre-trained and fine-tuned, the latent embeddings can be computed passing the multi-channel (MC) BW-NIR-D images from the WMCA database through the encoder, see [NGM19]_ for more details. These latent embeddings (feature vectors) are next used to train an MLP classifying input MC samples into bona-fide or attack classes.
+
+To compute the latent embeddings (encoder output) the following command can be used:
+
+.. code-block:: sh
+
+    ./bin/spoof.py \                                                    # spoof.py is used to run the preprocessor
+    batl-db-rgb-ir-d-grandtest \                                        # WMCA database instance allowing to load RGB-NIR-D channels
+    mc-pad-bw-nir-d-128x128-face-autoencoder-10-relu \                  # configuration defining preprocessor and extractor instances
+    --skip-projector-training --skip-projection --skip-score-computation --allow-missing-files \  # run only preprocessing and extraction steps
+    --sub-directory <PATH_TO_STORE_THE_RESULTS>                         # define your path here
+
+.. note::
+
+  Make sure the ``bob.learn.pytorch`` and ``bob.ip.pytorch_extractor`` packages are installed before running above command.
+
+Once above script is completed, the MC latent encodings to be used for MLP training are located in the folder ``<PATH_TO_STORE_THE_RESULTS>/extracted/``.
+Again, the training procedure is explained in the **MLP** section in the documentation of the ``bob.learn.pytorch`` package.
+