  Face Anti-Spoofing (FAS) is significant for the security of face recognition
systems. Convolutional Neural Networks (CNNs) have been introduced to the field
of the FAS and have achieved competitive performance. However, CNN-based
methods are vulnerable to the adversarial attack. Attackers could generate
adversarial-spoofing examples to circumvent a CNN-based face liveness detector.
Studies about the transferability of the adversarial attack reveal that
utilizing handcrafted feature-based methods could improve security in a
system-level. Therefore, handcrafted feature-based methods are worth our
exploration. In this paper, we introduce the deep forest, which is proposed as
an alternative towards CNNs by Zhou et al., in the problem of the FAS. To the
best of our knowledge, this is the first attempt at exploiting the deep forest
in the problem of FAS. Moreover, we propose to re-devise the representation
constructing by using LBP descriptors rather than the Grained-Scanning
Mechanism in the original scheme. Our method achieves competitive results. On
the benchmark database IDIAP REPLAY-ATTACK, 0\% Equal Error Rate (EER) is
achieved. This work provides a competitive option in a fusing scheme for
improving system-level security and offers important ideas to those who want to
explore methods besides CNNs.
