Publikationen

Autor	Huber, Marco; Fang, Meiling; Boutros, Fadi; Damer, Naser
Datum	2023
Art	Conference Paper
Abstrakt	Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explain-ability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations.
Konferenz	European Signal Processing Conference 2023
ISBN	978-9-4645-9360-0
Publisher	IEEE
Projekt	Next Generation Biometric Systems
Url	https://publica.fraunhofer.de/handle/publica/456561

Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection