Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Code

• github.com/ZitongYu/CDCN
  OfficialIn paperpytorch★ 602

Abstract

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, which is weak in describing detailed intrinsic information from modalities and easily being ineffective when the domain shifts (e.g., cross attack and cross ethnicity). In this paper, we extend the central difference convolutional networks (CDCN) yu2020searching to a multi-modal version, intending to capture intrinsic spoofing patterns among three modalities (RGB, depth and infrared). Meanwhile, we also give an elaborate study about single-modal based CDCN. Our approach won the first place in "Track Multi-Modal" as well as the second place in "Track Single-Modal (RGB)" of ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2020 liu2020cross. Our final submission obtains 1.020.59\% and 4.841.79\% ACER in "Track Multi-Modal" and "Track Single-Modal (RGB)", respectively. The codes are available athttps://github.com/ZitongYu/CDCN.

Tasks

Reproductions