Incorporating human salience into the training of CNNs has boosted
performance in difficult tasks such as biometric presentation attack detection.
However, collecting human annotations is a laborious task, not to mention the
questions of how and where (in the model architecture) to efficiently
incorporate this information into model's training once annotations are
obtained. In this paper, we introduce MENTOR (huMan pErceptioN-guided
preTraining fOr iris pResentation attack detection), which addresses both of
these issues through two unique rounds of training. First, we train an
autoencoder to learn human saliency maps given an input iris image (both real
and fake examples). Once this representation is learned, we utilize the trained
autoencoder in two different ways: (a) as a pre-trained backbone for an iris
presentation attack detector, and (b) as a human-inspired annotator of salient
features on unknown data. We show that MENTOR's benefits are threefold: (a)
significant boost in iris PAD performance when using the human
perception-trained encoder's weights compared to general-purpose weights (e.g.
ImageNet-sourced, or random), (b) capability of generating infinite number of
human-like saliency maps for unseen iris PAD samples to be used in any human
saliency-guided training paradigm, and (c) increase in efficiency of iris PAD
model training. Sources codes and weights are offered along with the paper.Comment: 8 pages, 3 figure