Abstract

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 …

Date

2021

Authors

Hengameh Mirzaalian, Mohamed E Hussein, Leonidas Spinoulas, Jonathan May, Wael Abd-Almageed

Conference

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

Pages

1-8

Publisher

IEEE