This section gives a brief overview on training the multi-channel CNN framework for PAD.
The framework described here is described in the publication [NGM20]_. It is recommended to check the publication for better understanding of the framework. However, the framework present in this package does not exactly match with the one presented
The framework described here is described in the publication [GMGNAM19]_. It is recommended to check the publication for better understanding of the framework. However, the framework present in this package does not exactly match with the one presented
in the reference paper. The framework presented here is more flexible, can accomodate more channels, can subselect channels
to perform scalability study. The network implemented here replicates the network in multiple channels instead of sharing the common weights, this modification is made to perform experiments with adapting different channels easily (however, both implementations are functionally same). Another difference is the way data balancing is implemented. In the publication [NGM20]_, databalancing is performed in the dataset using undersampling. However, in the current implementation, data imbalance in each mini-batch is handled explicitly by computing the weight for each samples and using it for the loss computation.
to perform scalability study. The network implemented here replicates the network in multiple channels instead of sharing the common weights, this modification is made to perform experiments with adapting different channels easily (however, both implementations are functionally same). Another difference is the way data balancing is implemented. In the publication [GMGNAM19]_, databalancing is performed in the dataset using undersampling. However, in the current implementation, data imbalance in each mini-batch is handled explicitly by computing the weight for each samples and using it for the loss computation.
Different stages for training MC-CNN are described below.
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@@ -142,9 +142,9 @@ Please inspect the corresponding configuration file, ``wmca_mccn.py`` for exampl
Running experiments with the trained model
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The trained model file can be used with ``MCCNExtractor`` to run PAD experiments with ``spoof.py`` script. A dummy algorithm is
The trained model file can be used with ``MCCNNExtractor`` to run PAD experiments with ``spoof.py`` script. A dummy algorithm is
added to forward the scalar values computed as the final scores.
.. [NGM20] *A. George, Z. Mostaani, D. Geissenbuhler, O. Nikisins, A. Anjos, S. Marcel*, **Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network**,
.. [GMGNAM19] *A. George, Z. Mostaani, D. Geissenbuhler, O. Nikisins, A. Anjos, S. Marcel*, **Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network**,
in: Submitted to: IEEE Transactions on Information Forensics & Security.
