Related papers: Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

URL: http://arxiv.org/abs/2003.03151v2
Date: Fri, 3 Jul 2020 10:02:30 GMT
Title: Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
Authors: Meiling Fang, Naser Damer, Florian Kirchbuchner, Arjan Kuijper
Abstract summary: We investigate and analyze the demographic bias in presentation attack detection (PAD) algorithms. We adapt the notions of differential performance and differential outcome to the PAD problem. Experiments show that female users will be significantly less protected by the PAD, in comparison to males.
Score: 15.15287401843062
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.

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