  Presentation attack detection (PAD) is a critical component in secure face
authentication. We present a PAD algorithm to distinguish face spoofs generated
by a photograph of a subject from live images. Our method uses an image
decomposition network to extract albedo and normal. The domain gap between the
real and spoof face images leads to easily identifiable differences, especially
between the recovered albedo maps. We enhance this domain gap by retraining
existing methods using supervised contrastive loss. We present empirical and
theoretical analysis that demonstrates that the contrast and lighting effects
can play a significant role in PAD; these show up particularly in the recovered
albedo. Finally, we demonstrate that by combining all of these methods we
achieve state-of-the-art results on datasets such as CelebA-Spoof, OULU and
CASIA-SURF.
