PT Journal
AU Ajian Liu
   Chenxu Zhao
   Zitong Yu
   Jun Wan
   Anyang Su
   Xing Liu
   Zichang Tan
   Sergio Escalera
   Junliang Xing
   Yanyan Liang
   Guodong Guo
   Zhen Lei
   Stan Z. Li
   Shenshen Du
TI Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
SO IEEE Transactions on Information Forensics and Security
JI TIForensicSEC
PY 2022
BP 2497
EP 2507
VL 17
DI 10.1109/TIFS.2022.3188149
AB Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a large-scale Hi gh- Fi delity Mask dataset, namely HiFiMask . Specifically, a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Along with the dataset, we propose a novel C ontrastive C ontext-aware L earning (CCL) framework. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method. The codes and dataset will be released soon.
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