Face spoofing detection, also known as liveness detection, is a challenging and one of the most active research areas in computer vision. In this paper, a novel texture descriptor, namely equilibrium difference local binary pattern (ED-LBP), is proposed for the representation and recognition of face texture. First, the adjacent pixels discrepancy in a facial image is fully considered and the discrepancy information is encoded into LBP. This novel texture feature extraction method has the advantage of getting more elaborate texture information without increasing the feature dimension. Second, the texture signatures in different color channels are fully investigated. More specifically, the feature histograms are calculated over each image band separately. Third, by the integration of the chromatic ED-LBP histograms and the two-level spatial pyramid, the local structure information of face is encoded in our approach which can well describe the differences between facial videos of valid users and impostors. Finally, the ED-LBP histograms from different color spaces are usually cascaded into a united feature vector which is feed into SVM for classification identification. Extensive experiments on four challenging and publicly available face anti-spoofing databases, namely CASIA FASD, Replay-Attack, Replay-Mobile, and OULU-NPU, demonstrate the effectiveness of our proposed approach. The results indicate that our methods are superior to state-of-the-art techniques and can effectively resist photo and video spoofing attacks in face recognition (FR).

中文翻译：

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基于彩色ED-LBP纹理特征的人脸欺骗检测

人脸欺骗检测，也称为活体检测，是计算机视觉中具有挑战性且最活跃的研究领域之一。在本文中，提出了一种新的纹理描述符，即均衡差分局部二值模式（ED-LBP），用于人脸纹理的表示和识别。首先，充分考虑人脸图像中相邻像素的差异，并将差异信息编码为LBP。这种新颖的纹理特征提取方法具有在不增加特征维数的情况下获得更精细的纹理信息的优点。其次，充分研究了不同颜色通道中的纹理特征。更具体地说，特征直方图是在每个图像带上单独计算的。第三，通过彩色 ED-LBP 直方图和两级空间金字塔的整合，人脸的局部结构信息在我们的方法中被编码，可以很好地描述有效用户和冒名顶替者的面部视频之间的差异。最后，来自不同颜色空间的 ED-LBP 直方图通常级联成一个统一的特征向量，该向量被输入 SVM 以进行分类识别。对四个具有挑战性和公开可用的人脸反欺骗数据库（即 CASIA FASD、Replay-Attack、Replay-Mobile 和 OULU-NPU）的大量实验证明了我们提出的方法的有效性。结果表明，我们的方法优于最先进的技术，并且可以有效抵抗人脸识别（FR）中的照片和视频欺骗攻击。