Multi-Modal Face Anti-Spoofing Based on Central Difference Networks
This work addresses security vulnerabilities in face recognition systems against presentation attacks, though it is incremental as it adapts an existing method to multi-modal data.
The paper tackled face anti-spoofing by extending central difference convolutional networks to multi-modal data (RGB, depth, infrared), achieving first place in a multi-modal challenge with 1.02±0.59% ACER and second place in a single-modal challenge with 4.84±1.79% ACER.
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) \cite{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 \cite{liu2020cross}. Our final submission obtains 1.02$\pm$0.59\% and 4.84$\pm$1.79\% ACER in "Track Multi-Modal" and "Track Single-Modal (RGB)", respectively. The codes are available at{https://github.com/ZitongYu/CDCN}.