Three discriminative representations for face presentation attack detection
are introduced in this paper. Firstly we design a descriptor called spatial
pyramid coding micro-texture (SPMT) feature to characterize local appearance
information. Secondly we utilize the SSD, which is a deep learning framework
for detection, to excavate context cues and conduct end-to-end face
presentation attack detection. Finally we design a descriptor called template
face matched binocular depth (TFBD) feature to characterize stereo structures
of real and fake faces. For accurate presentation attack detection, we also
design two kinds of representation combinations. Firstly, we propose a
decision-level cascade strategy to combine SPMT with SSD. Secondly, we use a
simple score fusion strategy to combine face structure cues (TFBD) with local
micro-texture features (SPMT). To demonstrate the effectiveness of our design,
we evaluate the representation combination of SPMT and SSD on three public
datasets, which outperforms all other state-of-the-art methods. In addition, we
evaluate the representation combination of SPMT and TFBD on our dataset and
excellent performance is also achieved.

本文引入了三种判别性表示来进行人脸防攻击检测。首先，我们设计了一个称为 SPMT 特征来表征局部外观信息的描述符。第二，我们利用 SSD 这种深度学习框架来发掘上下文线索，并进行端到端的人脸防攻击检测。最后，我们设计了一个称为 TFBD 特征来表征真假面孔的立体结构。为了精确的防攻击检测，我们还设计了两种表示组合。首先，我们提出了一个决策级串联策略来将 SPMT 与 SSD 相结合。其次，我们使用简单的分数融合策略将人脸结构线索（TFBD）与局部微纹理特征（SPMT）结合起来。为了证明我们的设计的有效性，我们在三个公共数据集上评估了 SPMT 和 SSD 的表示组合，其表现优于所有其他最先进的方法。此外，我们还评估了 SPMT 和 TFBD 的表示组合，在我们的数据集上也取得了出色的性能。