人脸反欺诈检测的当前进展

多模态数据集中的概述：

Kim et al. [22] aim to distinguish between the facial skin and mask materials by exploiting their reflectance.
Kose et al. [27] propose a 2D+3D face mask attacks dataset
to study the effects of mask attacks, but the realted dataset
is not public. 3DMAD [14] is the first publicly available 3D
masks dataset, which is recorded using Microsoft Kinect
sensor and consists of the Depth and RGB modalities. Another multi-modal face PAD dataset is Msspoof [8] that contains visible (VIS) and near-infrared (NIR) images of real
accesses and printed spoofing attacks with ≤ 21 objects.

Face anti-spoofing has been studied for decades. Some
previous work [35, 42, 24, 1] attempt to detect the evidence
of liveness (i.e., eye-blinking). Another works are based
on contextual [36, 25] and [43, 12, 21] information. To
improve the robustness to illumination variation, some algorithms adopt HSV and YCbCr color space [3, 4], and
Fourier spectrum [28]. All fo these methods use handcrafted features, such as LBP [34, 7, 47, 32], HoG [47, 32,
39] and GLCM [39]. They are fast enough and have relatively satisfactory performance only on small public face
spoof datasets with poor generalizability.
Some fusion methods are propposed to obtain a more
general countermeasure effective against a variation of attack types. Tronci et al. [41] propose a linear fusion at
a frame and video level combination between static and
video analysis. Schwartz et al. [39] introduce feature level
fusion by using Partial Least Squares (PLS) regression
based on a set of low-level feature descriptors. Some other
works [10, 26] obtain an effective fusion scheme by measuring the level of independence of two anti-counterfeiting systems. However, these fusion methods focus on score or feature level, not modality level, due to the lack of multi-modal
datasets. It is urgent to propose a multi-modal dataset.
Recently, CNN-based methods [15, 29, 37, 46, 31, 19]
are presented in face PAD community. They treat face PAD
as a binary classification problem and achieve remarkable
improvements in the intra-testing. Liu et al. [31] design
a novel network architecture to leverage two auxiliary information (the Depth map and rPPG signal) as supervision with the goals of improved generalization. Amin et
al. [19] introduce a new perspective for solving the face
anti-spoofing by inversely decomposing a spoof face into
the live face and the spoof noise pattern. However, they
exhibit a poor generalization ability during the cross-testing
due to the over-fitting to training data. This problem has still
not been solved, even if some works [29, 37] adopt transfer learning to train the CNN model from ImageNet [13].
These works bring us the insight that we need to collect a
larger PAD dataset.

 

8. Acknowledgements
This work has been partially supported by Science
and Technology Development Fund of Macau (Grant No.
0025/2018/A1), by the Chinese National Natural Science
Foundation Projects #61502491, #61876179, by the Spanish project TIN2016-74946-P (MINECO/FEDER, UE) and
CERCA Programme / Generalitat de Catalunya. We gratefully acknowledge Surfing Technology Beijing co., Ltd
(www.surfing.ai) to capture and provide us this high quality
dataset for this research, and also acknowledge the support
of NVIDIA Corporation with the donation of the GPU used
for this research.
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