Abstract:Aiming at the problem that face recognition systems are suspectable to be affected by forgery attacks, a face liveness detection method based on near-infrared and visible binocular vision was proposed. Firstly, the binocular device was used to obtain the face images of near-infrared and visible light synchronously. Then, the facial feature points of two images were extracted, and the binocular relation was used to match the feature points and obtain their depth information, which was used for three-dimensional point cloud reconstruction. Secondly, all facial feature points were divided into four regions, and the average variance of facial feature points in the depth direction within each region was calculated. Thirdly, the key feature points of face were selected. With the nasal tip point as the reference point, the spatial distances between the nasal tip point and the key feature points were calculated. Finally, the feature vectors were constructed by using the depth value variances and spatial distances of facial feature points. And Support Vector Machine (SVM) was used for the judgment of real faces. The experimental results show that the proposed method can detect real faces accurately and resist the attacks of fake faces effectively, achieves the recognition rate of 99.0% in experimental tests, and is superior in accuracy and robustness to the similar algorithm using depth information of facial feature points for detection.
[1] KIRBY M,SIROVICH L. Application of the Karhunen-Loeve procedure for the characterization of human faces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(1):103-108. [2] SCHUCKERS S A C. Spoofing and anti-spoofing measures[J]. Information Security Technical Report,2002,7(4):56-62. [3] HUANG D,YAN H. NURBS curve controlled modelling for facial animation[J]. Computers and Graphics,2003,27(3):373-385. [4] BLANZ V,VETTER T,ROCKWOOD A. A morphable model for the synthesis of 3D faces[C]//Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. New York:ACM,1999:187-194. [5] LI S Z,CHU R,LIAO S,et al. Illumination invariant face recognition using near-infrared images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):627-639. [6] 蒋尚达. 基于视频的活体人脸检测算法研究[D]. 成都:电子科技大学,2018:23-26.(JIANG S D. Research on face anti-spoofing algorithm based on video[D]. Chengdu:University of Electronic Science and Technology of China,2018:23-26.) [7] 孔祥山. 基于红外与可见光图像的无人机测距技术研究[D]. 成都:电子科技大学,2018:1-2.(KONG X S. Research on UAV ranging technology based on infrared and visible images[D]. Chengdu:University of Electronic Science and Technology of China, 2018:1-2.) [8] 荣传振, 贾永兴, 吴城, 等. 红外与可见光图像分解与融合方法研究[J]. 数据采集与处理,2019,34(1):146-156.(RONG C Z, JIA Y X,WU C,et al. Decomposition and fusion methods for infrared and visible images[J]. Journal of Data Acquisition and Processing,2019,34(1):146-156.) [9] 王悦扬. 基于多光谱成像的人脸活体检测[D]. 北京:北京交通大学,2014:3-5.(WANG Y Y. Face liveness detection based on multispectral imaging[D]. Beijing:Beijing Jiaotong University, 2014:3-5.) [10] WU Y,JI Q. Robust facial landmark detection under significant head poses and occlusion[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2015:3658-3666. [11] LIN L,XIAO R,WEN F,et al. Face alignment via componentbased discriminative search[C]//Proceedings of the 10th European Conference on Computer Vision,LNCS 5303. Berlin:Springer,2008:72-85. [12] VALSTAR M,MARTINEZ B,BINEFA X,et al. Facial point detection using boosted regression and graph models[C]//Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2010:2729-2736. [13] CAO X,WEI Y,WEN F,et al. Face alignment by explicit shape regression[J]. International Journal of Computer Vision,2014, 107(2):177-190. [14] BELHUMEUR P N,JACOBS D W,KRIEGMAN D J,et al. Localizing parts of faces using a consensus of exemplars[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013, 35(12):2930-2940. [15] SUN Y,WANG X,TANG X. Deep convolutional network cascade for facial point detection[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2013:3476-3483. [16] CORTES C,VAPNIK V N. Support-vector networks[J]. Machine Learning,1995,20(3):273-297. [17] 李俊山, 朱英宏, 朱艺娟, 等. 红外与可见光图像自相似性特征的描述与匹配[J]. 激光与红外,2013,43(3):339-343.(LI J S,ZHU Y H,ZHU Y J,et al. Description and matching of selfsimilarities for IR and visual images[J]. Laser and Infrared, 2013,43(3):339-343.) [18] 蒋萌, 王尧尧, 陈柏. 基于双目视觉的目标识别与定位研究[J]. 机电工程,2018,35(4):414-419.(JIANG M,WANG Y Y,CHEN B. Recognition and orientation of object based on binocular vision[J]. Journal of Mechanical & Electrical Engineering, 2018,35(4):414-419.)