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A large number of deep neural network based techniques have been developed toaddress the challenging problem of face presentation attack detection (PAD).Whereas such techniques' focus has been on improving PAD performance in termsof classification accuracy and robustness against unseen attacks andenvironmental conditions, there exists little attention on the explainabilityof PAD predictions. In this paper, we tackle the problem of explaining PADpredictions through natural language. Our approach passes featurerepresentations of a deep layer of the PAD model to a language model togenerate text describing the reasoning behind the PAD prediction. Due to thelimited amount of annotated data in our study, we apply a light-weight LSTMnetwork as our natural language generation model. We investigate how thequality of the generated explanations is affected by different loss functions,including the commonly used word-wise cross entropy loss, a sentencediscriminative loss, and a sentence semantic loss. We perform our experimentsusing face images from a dataset consisting of 1,105 bona-fide and 924presentation attack samples. Our quantitative and qualitative results show theeffectiveness of our model for generating proper PAD explanations through textas 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 canbe obtained through our GitHub page.

Explaining Face Presentation Attack Detection Using Natural Language