  Face presentation attacks (PA), also known as spoofing attacks, pose a
substantial threat to biometric systems that rely on facial recognition
systems, such as access control systems, mobile payments, and identity
verification systems. To mitigate the spoofing risk, several video-based
methods have been presented in the literature that analyze facial motion in
successive video frames. However, estimating the motion between adjacent frames
is a challenging task and requires high computational cost. In this paper, we
rephrase the face anti-spoofing task as a motion prediction problem and
introduce a deep ensemble learning model with a frame skipping mechanism. In
particular, the proposed frame skipping adopts a uniform sampling approach by
dividing the original video into video clips of fixed size. By doing so, every
nth frame of the clip is selected to ensure that the temporal patterns can
easily be perceived during the training of three different recurrent neural
networks (RNNs). Motivated by the performance of individual RNNs, a meta-model
is developed to improve the overall detection performance by combining the
prediction of individual RNNs. Extensive experiments were performed on four
datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%),
Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total
error rates (HTERs) in the most challenging cross-dataset testing scenario.
