  A large number of deep neural network based techniques have been developed to
address the challenging problem of face presentation attack detection (PAD).
Whereas such techniques' focus has been on improving PAD performance in terms
of classification accuracy and robustness against unseen attacks and
environmental conditions, there exists little attention on the explainability
of PAD predictions. In this paper, we tackle the problem of explaining PAD
predictions through natural language. Our approach passes feature
representations of a deep layer of the PAD model to a language model to
generate text describing the reasoning behind the PAD prediction. Due to the
limited amount of annotated data in our study, we apply a light-weight LSTM
network as our natural language generation model. We investigate how the
quality of the generated explanations is affected by different loss functions,
including the commonly used word-wise cross entropy loss, a sentence
discriminative loss, and a sentence semantic loss. We perform our experiments
using face images from a dataset consisting of 1,105 bona-fide and 924
presentation attack samples. Our quantitative and qualitative results show the
effectiveness of our model for generating proper PAD explanations through text
as well as the power of the sentence-wise losses. To the best of our knowledge,
this is the first introduction of a joint biometrics-NLP task. Our dataset can
be obtained through our GitHub page.
