  Without deploying face anti-spoofing countermeasures, face recognition
systems can be spoofed by presenting a printed photo, a video, or a silicon
mask of a genuine user. Thus, face presentation attack detection (PAD) plays a
vital role in providing secure facial access to digital devices. Most existing
video-based PAD countermeasures lack the ability to cope with long-range
temporal variations in videos. Moreover, the key-frame sampling prior to the
feature extraction step has not been widely studied in the face anti-spoofing
domain. To mitigate these issues, this paper provides a data sampling approach
by proposing a video processing scheme that models the long-range temporal
variations based on Gaussian Weighting Function. Specifically, the proposed
scheme encodes the consecutive t frames of video sequences into a single RGB
image based on a Gaussian-weighted summation of the t frames. Using simply the
data sampling scheme alone, we demonstrate that state-of-the-art performance
can be achieved without any bells and whistles in both intra-database and
inter-database testing scenarios for the three public benchmark datasets;
namely, Replay-Attack, MSU-MFSD, and CASIA-FASD. In particular, the proposed
scheme provides a much lower error (from 15.2% to 6.7% on CASIA-FASD and 5.9%
to 4.9% on Replay-Attack) compared to baselines in cross-database scenarios.
