Publikationen

Autor	Fang, Meiling; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan
Datum	2020
Art	Conference Paper
Abstrakt	With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the NDCLD- 2013 [18] database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.
Konferenz	European Signal Processing Conference (EUSIPCO) 2020
Projekt	ATHENE
Url	https://publica.fraunhofer.de/handle/publica/409735

Demographic Bias in Presentation Attack Detection of Iris Recognition Systems