Computer Science ›› 2020, Vol. 47 ›› Issue (2): 112-117.doi: 10.11896/jsjkx.181202339

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Face Liveness Detection Based on Image Diffusion Speed Model and Texture Information

LI Xin-dou,GAO Chen-qiang,ZHOU Feng-shun,HAN Hui,TANG Lin   

  1. (Chongqing Key Laboratory of Signal and Information Processing,College of Information and Communication Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
  • Received:2018-12-17 Online:2020-02-15 Published:2020-03-18
  • About author:LI Xin-dou,born in 1992,postgraduate.His main research interests indude face liveness detection;GAO Chen-qiang,born in 1981,Ph.D,professor.His research interests include image processing,infrared target detection and event detection.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61571071) and Natural Science Foundation of Chongqing Science and Technology Commission (cstc2014jcyjA40048).

Abstract: To solve the problem of fraud in face authentication,this paper proposed a face liveness detection algorithm based on image diffusion speed model and texture information.The spatial structures of real face and fake face images are different.In order to extract difference features,anisotropic diffusion is used to enhance image edge information.And then,the difference between the original image and the diffused image is used as the image diffusion speed,and a diffusion velocity model is contructed.Then,local binary pattern algorithm is used to extract the diffusion speed feature and train a classifier.There are many differences between real face images and fake face images.In order to further improve the generalization ability of face liveness detection,the blur degree and color feature of face image are extracted synchronously.These features are combined by cascading feature matrix and another classifier is trained.Finally,a judgment is made based on the probabilities weighted fusion result of classifier output.Experimental results show that the proposed algorithm can detect spoofing faces quickly and efficiently.

Key words: Anisotropic diffusion, Face liveness detection, Face recognition, Local binary pattern

CLC Number: 

  • TP391.41
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