基于差分量化局部二值模式的人脸反欺诈算法研究

随着人脸识别技术已经融入到人们日常生活中，人脸欺诈检测作为人脸识别前的一个关键步骤越来越受到重视.针对打印攻击和视频攻击，提出了一种通过量化局部像素之间的差值来细化传统局部二值模式(local binary pattern, LBP)特征的差分量化局部二值模式(difference quantization local binary pattern, DQ_LBP)算法.DQ_LBP能够在不增加LBP维度的基础上提取像素之间的差值信息，以便更精确地描述图像的局部纹理特征.此外，使用空间金字塔算法统计了不同彩色空间中的DQ_LBP特征并将其融合成统一的特征向量，从而更加充分地描述了人脸的局部彩色纹理信息及其空间结构信息，进一步提高了算法的检测性能.实验结果表明：该算法在CASIA FASD,Replay-Attack,Replay-Mobile三个具有挑战性的人脸反欺诈数据库中都取得了较为优异的结果，而且在实时性设备的应用上具有很大的潜能.

Research on Face Anti-Spoofing Algorithm Based on DQ_LBP

As face recognition technology has been integrated into human daily life, face spoofing detection as a key step before face recognition has attracted more and more attention. For print attack and video attack, we propose a difference quantization local binary pattern (DQ_LBP) algorithm for refining the feature of traditional local binary pattern (LBP) by quantifying the difference between the value of central pixel and its neighborhood pixels. DQ_LBP can extract the difference information between the local pixels without increasing the original dimension of LBP, and thus be able to describe the local texture features of images more accurately. In addition, we use the spatial pyramid (SP) algorithm to calculate the histogram of DQ_LBP features in different color spaces and cascade them into a unified feature vector, so as to obtain more elaborate local color texture information and spatial structure information from the face sample, thus, the fraud face detection performance of the algorithm in this paper has been further improved. Extensive experiments are conducted on three challenging face anti-spoofing databases (CASIA FASD, Replay-Attack, and Replay-Mobile) and show that our algorithm has better performance compared with the state of the art. Moreover, it has great potential in the application of real-time devices.