  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.
