Face Anti-Spoofing Liveness Detection Combining DQ_CoALBP with LPQ Descriptors

doi:10.3778/j.issn.1002-8331.2012-0123

Abstract

Abstract: Aiming at the task of face anti-spoofing liveness detection under image and video attacks, a descriptor of different quantization co-occurrence of adjacent local binary pattern（DQ_CoALBP） is proposed, which comprehensively considers the difference between local center point from different directions and surrounding points of the image. In addition, in order to describe the color texture information of the face more fully, the descriptor is combined with local phase quantization（LPQ） histogram features in the channel of color space. And support vector machine（SVM） classifier is used for face anti-spoofing discrimination. The experimental results on the public CASIA-FASD and Replay-Attack datasets show that the performance of DQ_CoALBP descriptor is better than LBP, LPQ, CoALBP and DQ_LBP. When DQ_CoALBP descriptor is combined with LPQ on YCbCr color space, the equal error rate（EER） and half total error rate（HTER） on the CASIA-FASD dataset are reduced to 2.5% and 3.7% respectively, and error free detection is realized on the Replay-Attack dataset, which is better than some models based on deep convolution neural network.

Key words: face anti-spoofing liveness detection, local binary pattern, different quantization, feature fusion, texture feature, YCbCr

摘要： 面向图片与视频攻击下的人脸活体检测任务，提出了一种差分量化相邻局部二值模式（DQ_CoALBP）算子，综合不同方向上的图像局部中心点与周围点之间的差值，同时为了更加充分地描述人脸的彩色纹理信息，在颜色空间通道上将该算子与局部相位量化（LPQ）直方图特征相融合，并利用支持向量机（SVM）分类器实现人脸反欺诈判别。在公开CASIA-FASD与Replay-Attack数据集上的实验结果表明，DQ_CoALBP算子的表现均优于LBP、LPQ、CoALBP与DQ_LBP四种算子。采用YCbCr颜色空间在融合DQ_CoALBP与LPQ算子时，CASIA-FASD数据集上的等错误率（EER）和半错误率（HTER）分别降至2.5%和3.7%，Replay-Attack数据集上实现了无差错检测，优于一些深度卷积神经网络模型。

关键词: 人脸活体检测, 局部二值模式, 差分量化, 特征融合, 纹理特征, YCbCr

FENG Jun, DONG Zhiyi, LIU Tiantian, HAN Chaoqun, HU Jingjing. Face Anti-Spoofing Liveness Detection Combining DQ_CoALBP with LPQ Descriptors[J]. Computer Engineering and Applications, 2022, 58(14): 134-143.

封筠, 董祉怡, 刘甜甜, 韩超群, 胡晶晶. 融合DQ_CoALBP和LPQ算子的人脸活体检测[J]. 计算机工程与应用, 2022, 58(14): 134-143.

References

[1] 王先基，陈友斌.一种活体人脸检测方法与系统：CN103440479B[P].
2016-12-28.
WANG X J，CHEN Y B.Method and system for face detection：CN103440479B[P].2016-12-28.
[2] NG E S，CHIA A Y S.Face verification using temporal affective cues[C]//21st International Conference on Pattern Recognition，Tsukuba，Nov 11-15，2012.New York：IEEE，2012：1249-1252.
[3] 张进，张娜娜.优化特征提取的互动式人脸活体检测研究[J].计算机工程与应用，2019，55（13）：193-200.
ZHANG J，ZHANG N N.Research on interactive face detection based on optimized feature extraction[J].Computer Engineering and Applications，2019，55（13）：193-200.
[4] MATTA J，HADID A，PIETIKAINEN M.Face spoofing detection from single images using micro-texture analysis[C]//2011 International Joint Conference on Biometrics，Washington，Oct 11-13，2011.New York：IEEE，2011：1-7.
[5] BOULKENAFET Z，KOMULAINEN J，HADID A.Face spoofing detection using colour texture analysis[J].IEEE Transactions on Information Forensics and Security，2016，11（8）：1818-1830.
[6] BOULKENAFET Z，KOMULAINEN J，HADID A.Face anti-spoofing using speeded-up robust features and fisher vector encoding[J].IEEE Signal Processing Letters，2017，24（2）：141-145.
[7] WEN D，HAN H，JAIN A K.Face spoof detection with image distortion analysis[J].IEEE Transactions on Information Forensics and Security，2015，10（4）：746-761.
[8] LI H，WANG S，KOT A C.Face spoofing detection with image quality regression[C]//6th International Conference on Image Processing Theory Tools and Applications，Oulu，Dec 12-15，2016.New York：IEEE，2016：1-6.
[9] TIAGO D F P，ANJOS A，DE MARTINO J M，et al.Can face anti-spoofing countermeasures work in a real world scenario?[C]//2013 International Conference on Biometrics，Madrid，Jun 4-7，2013.New York：IEEE，2013：1-8.
[10] LI X B，KOMULAINEN J，ZHAO G Y，et al.Generalized face anti-spoofing by detecting pulse from face videos[C]//23rd International Conference on Pattern Recognition，Cancun，Dec 4-8，2016.New York：IEEE，2016：4244-4249.
[11] YANG J，LEI Z，LI S Z.Learn convolutional neural network for face anti-spoofing[EB/OL].（2014-08-26）[2020-11-23].https：//arxiv.org/abs/1408.5601v2.
[12] PARKHI O M，VEDALDI A，ZISSERMAN A.Deep face recognition[C]//Proceedings of British Machine Vision Conference，Swansea，Sep 7-10，2015.
[13] LI L，FENG X，JIANG X，et al.Face anti-spoofing via deep local binary patterns[C]//IEEE International Conference on Image Processing，Beijing，Sep 17-20，2017.Singapore：Springer，2017：91-111.
[14] NOSAKA R，OHKAWA Y，FUKUI K.Feature extraction based on co-occurrence of adjacent local binary patterns[C]//Pacific-Rim Symposium on Image and Video Technology，Gwangju，Nov 20-23，2011.Berlin，Heidelberg：Springer，2011：82-91.
[15] 束鑫，唐慧，杨习贝，等.基于差分量化局部二值模式的人脸反欺诈算法研究[J].计算机研究与发展，2020，57（7）：1508-1521.
SHU X，TANG H，YANG X B，et al.Research on face anti-spoofing algorithm based on DQ_LBP[J].Journal of Computer Research and Development，2020，57（7）：1508-1521.
[16] OJANSIVU V，HEIKKILA J.Blur insensitive texture classification using local phase quantization[C]//International Conference on Image and Signal Processing，San Diego，Oct 12-15，2008.Berlin，Heidelberg：Springer，2008：236-243.
[17] DALAL N，TRIGGS B.Histograms of oriented gradients for human detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition，San Diego，Sep 21-23，2005.New York：IEEE，2005：886-893.
[18] ZHANG Z，YAN J，LIU S，et al.A face anti-spoofing database with diverse attacks[C]//5th IAPR International Conference on Biometrics，New Delhi，Mar 29-Apr 1，2012.New York：IEEE，2012：26-31.
[19] CHINGOVSK A I，ANJOS A，MARCEL S.On the effectiveness of local binary patterns in face anti-spoofing[C]//International Conference of the Biometrics Special Interest Group，Darmstadt，Sep 6-7，2012.New York：IEEE，2012：1-7.
[20] 田野，项世军.基于LBP和多层DCT的人脸活体检测算法[J].计算机研究与发展，2018，55（3）：643-650.
TIAN Y，XIANG S J.LBP and multilayer DCT based anti-spoofing countermeasure in face liveness detection[J].Journal of Computer Research and Development，2018，55（3）：643-650.
[21] ATOUM Y，LIU Y J，JOURABLOO A，et al.Face anti-spoofing using patch and depth-based CNNs[C]//IEEE International Joint Conference on Biometrics，Denver，Oct 1-4，2017.New York：IEEE，2017：319-328.
[22] GAN J Y，LI S L，ZHAI Y K，et al.3D convolutional neural network based on face anti-spoofing[C]//International Conference on Multimedia and Image Processing，Wuhan，Mar 17-19，2017.New York：IEEE，2017：1-5.
[23] REHMAN Y A U，PO L M，LIU M Y.LiveNet：improving features generalization for face liveness detection using convolution neural networks[J].Expert Systems with Applications，2018，108：159-169.