Xuan Li

TL;DR: This work introduces the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, and proposes a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias.

TL;DR: The Chalearn Face Anti-spoofing Attack Detection Challenge (CASIA-SURF CeFA) as mentioned in this paper was organized to measure the ethnic bias in face anti-spouting.

TL;DR: An overview of the Chalearn Face Anti-spoofing Attack Detection Challenge, including its design, evaluation protocol and a summary of results is presented, which analyzed the top ranked solutions and drew conclusions derived from the competition.

TL;DR: Wang et al. as mentioned in this paper proposed a 3D Point Cloud Network (3DPC-Net) which is an encoder-decoder network that can predict the 3DPC maps to discriminate live faces from spoofing ones.

TL;DR: This work proposes a static-dynamic fusion mechanism for multi-modal face anti-spoofing, inspired by motion divergences between real and fake faces, and develops a partially shared fusion method to learn complementary information from multiple modalities.