  Face anti-spoofing researches are widely used in face recognition and has
received more attention from industry and academics. In this paper, we propose
the EulerNet, a new temporal feature fusion network in which the differential
filter and residual pyramid are used to extract and amplify abnormal clues from
continuous frames, respectively. A lightweight sample labeling method based on
face landmarks is designed to label large-scale samples at a lower cost and has
better results than other methods such as 3D camera. Finally, we collect 30,000
live and spoofing samples using various mobile ends to create a dataset that
replicates various forms of attacks in a real-world setting. Extensive
experiments on public OULU-NPU show that our algorithm is superior to the state
of art and our solution has already been deployed in real-world systems
servicing millions of users.
