TY  - CONF
AU  - Shifeng Zhang
AU  - Xiaobo Wang
AU  - Ajian Liu
AU  - Chenxu Zhao
AU  - Jun Wan
AU  - Sergio Escalera
AU  - Hailin Shi
AU  - Zezheng Wang
AU  - Stan Z. Li
A2  - CVPR
PY  - 2019//
TI  - A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
BT  - 32nd IEEE Conference on Computer Vision and Pattern Recognition
SP  - 919
EP  - 928
N2  - Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/chalearnfacespoofingattackdete/.
UR  - https://ieeexplore.ieee.org/abstract/document/8953793
L1  - http://refbase.cvc.uab.es/files/ZWL2019.pdf
UR  - http://dx.doi.org/10.1109/CVPR.2019.00101
N1  - HuPBA; no proj
ID  - Shifeng Zhang2019
ER  -