  Automatic methods for detecting presentation attacks are essential to ensure
the reliable use of facial recognition technology. Most of the methods
available in the literature for presentation attack detection (PAD) fails in
generalizing to unseen attacks. In recent years, multi-channel methods have
been proposed to improve the robustness of PAD systems. Often, only a limited
amount of data is available for additional channels, which limits the
effectiveness of these methods. In this work, we present a new framework for
PAD that uses RGB and depth channels together with a novel loss function. The
new architecture uses complementary information from the two modalities while
reducing the impact of overfitting. Essentially, a cross-modal focal loss
function is proposed to modulate the loss contribution of each channel as a
function of the confidence of individual channels. Extensive evaluations in two
publicly available datasets demonstrate the effectiveness of the proposed
approach.
