  Ethnic bias has proven to negatively affect the performance of face
recognition systems, and it remains an open research problem in face
anti-spoofing. In order to study the ethnic bias for face anti-spoofing, we
introduce the largest up to date CASIA-SURF Cross-ethnicity Face Anti-spoofing
(CeFA) dataset (briefly named CeFA), covering $3$ ethnicities, $3$ modalities,
$1,607$ subjects, and 2D plus 3D attack types. Four protocols are introduced to
measure the affect under varied evaluation conditions, such as cross-ethnicity,
unknown spoofs or both of them. To the best of our knowledge, CeFA is the first
dataset including explicit ethnic labels in current published/released datasets
for face anti-spoofing. Then, we propose a novel multi-modal fusion method as a
strong baseline to alleviate these bias, namely, the static-dynamic fusion
mechanism applied in each modality (i.e., RGB, Depth and infrared image).
Later, a partially shared fusion strategy is proposed to learn complementary
information from multiple modalities. Extensive experiments demonstrate that
the proposed method achieves state-of-the-art results on the CASIA-SURF,
OULU-NPU, SiW and the CeFA dataset.
