TY  - JOUR
AU  - Ajian Liu
AU  - Chenxu Zhao
AU  - Zitong Yu
AU  - Jun Wan
AU  - Anyang Su
AU  - Xing Liu
AU  - Zichang Tan
AU  - Sergio Escalera
AU  - Junliang Xing
AU  - Yanyan Liang
AU  - Guodong Guo
AU  - Zhen Lei
AU  - Stan Z. Li
AU  - Shenshen Du
PY  - 2022//
TI  - Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
T2  - TIForensicSEC
JO  - IEEE Transactions on Information Forensics and Security
SP  - 2497
EP  - 2507
VL  - 17
PB  - IEEE
N2  - 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.
UR  - http://dx.doi.org/10.1109/TIFS.2022.3188149
N1  - HuPBA
ID  - Ajian Liu2022
ER  -