The vulnerability of face recognition-based unlocking systems to spoofing attacks is a serious problem. With the development of science and technology, face pictures on the Internet are particularly easy to obtain and recreate. A face spoofing attack happens when someone fakes a face and tries to pass the verification of the face recognition system, which seriously threatens the security of users’ information. We propose a method based on quantifying the difference between neighborhood pixels around the center of a local region, namely, weighted neighborhood difference quantization local binary pattern (LBP), against print attacks, and video-replay attacks. The proposed method quantifies the differences between neighborhood pixels without using the center one and uses a linear weighting scheme to improve the discriminant capability of the traditional LBP. The combination of the proposed algorithm and the spatial pyramid further improves the performance of face spoofing detection. We also conducted a lot of experiments in different color spaces to illustrate the role of color in face spoofing detection. The improved method achieves better results in three challenging face anti-spoofing databases, CASIA FASD, Replay-Attack, and Replay-Mobile, respectively.

中文翻译：

---

基于加权邻域像素差模式的人脸反欺骗

基于面部识别的解锁系统容易遭受欺骗攻击，这是一个严重的问题。随着科学技术的发展，互联网上的人脸图片特别容易获得和再现。当有人伪造人脸并试图通过人脸识别系统的验证时，就会发生人脸欺骗攻击，这严重威胁了用户信息的安全性。我们提出一种基于量化局部区域中心附近的邻域像素之间的差异的方法，即加权邻域差异量化局部二进制模式（LBP），以抵抗打印攻击和视频重放攻击。所提出的方法无需使用中心像素就可以量化相邻像素之间的差异，并使用线性加权方案来提高传统LBP的判别能力。提出的算法与空间金字塔相结合，进一步提高了人脸欺骗检测的性能。我们还在不同的颜色空间中进行了许多实验，以说明颜色在面部欺骗检测中的作用。改进的方法分别在三个具有挑战性的面部防欺骗数据库CASIA FASD，Replay-Attack和Replay-Mobile中获得了更好的结果。我们还在不同的颜色空间中进行了许多实验，以说明颜色在面部欺骗检测中的作用。改进的方法分别在三个具有挑战性的面部防欺骗数据库CASIA FASD，Replay-Attack和Replay-Mobile中获得了更好的结果。我们还在不同的颜色空间中进行了许多实验，以说明颜色在面部欺骗检测中的作用。改进的方法分别在三个具有挑战性的面部防欺骗数据库CASIA FASD，Replay-Attack和Replay-Mobile中获得了更好的结果。