Detection of Face Spoofing Using Visual Dynamics

Rendering a face recognition system robust is vital in order to safeguard it against spoof attacks carried out using printed pictures of a victim (also known as print attack) or a replayed video of the person (replay attack). A key property in distinguishing a live, valid access from printed media or replayed videos is by exploiting the information dynamics of the video content, such as blinking eyes, moving lips, and facial dynamics. We advance the state of the art in facial antispoofing by applying a recently developed algorithm called dynamic mode decomposition (DMD) as a general purpose, entirely data-driven approach to capture the above liveness cues. We propose a classification pipeline consisting of DMD, local binary patterns (LBPs), and support vector machines (SVMs) with a histogram intersection kernel. A unique property of DMD is its ability to conveniently represent the temporal information of the entire video as a single image with the same dimensions as those images contained in the video. The pipeline of DMD + LBP + SVM proves to be efficient, convenient to use, and effective. In fact only the spatial configuration for LBP needs to be tuned. The effectiveness of the methodology was demonstrated using three publicly available databases: (1) print-attack; (2) replay-attack; and (3) CASIA-FASD, attaining comparable results with the state of the art, following the respective published experimental protocols.

为了防范使用受害者打印照片（也称为打印攻击）或该人重放视频（重放攻击）实施的欺骗攻击，使人脸识别系统具有鲁棒性至关重要。区分真实有效的访问与打印媒体或重放视频的一个关键特性是利用视频内容的信息动态，例如眨眼、嘴唇移动和面部动态。我们通过应用一种最近开发的算法——动态模式分解（DMD），作为一种通用的、完全由数据驱动的方法来捕捉上述活体线索，从而推进了面部反欺骗技术的发展。我们提出了一个由DMD、局部二值模式（LBPs）和具有直方图相交核的支持向量机（SVMs）组成的分类流程。DMD的一个独特特性是它能够方便地将整个视频的时间信息表示为一个与视频中图像具有相同维度的单一图像。DMD + LBP + SVM的流程被证明是高效、使用方便且有效的。实际上，只需要调整LBP的空间配置。使用三个公开可用的数据库证明了该方法的有效性：（1）打印攻击数据库；（2）重放攻击数据库；（3）CASIA - FASD数据库，按照各自公布的实验协议，取得了与现有技术相当的结果。