Liveness Measurements Using Optical Flow for Biometric Person Authentication

Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenti ng a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely impor tant. In this paper we present a method that differentiates between video sequences showing real persons a nd their photographs. First we calculate the optical flow of the face region using the Farneback algorithm. Then w e convert the motion information into images and perform the initial data selection. Finally, we apply the Sup port Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach c ould be successfully applied in practice.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  Jang-Hee Yoo,et al.  Liveness Detection for Embedded Face Recognition System , 2008 .

[3]  Lin Sun,et al.  Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[4]  Haitao Wang,et al.  Face recognition under varying lighting conditions using self quotient image , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[5]  Maciej Smiatacz,et al.  Modular machine learning system for training object detection algorithms on a supercomputer , 2010 .

[6]  Hong Li,et al.  A liveness detection method for face recognition based on optical flow field , 2009, 2009 International Conference on Image Analysis and Signal Processing.

[7]  Jhing-Fa Wang,et al.  SVM-based one-against-many algorithm for liveness face authentication , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[8]  Youren Wang,et al.  Analog Circuit Fault Classification Using Improved One-Against-One Support Vector Machines , 2011 .

[9]  Gunnar Farnebäck Spatial Domain Methods for Orientation and Velocity Estimation , 1999 .

[10]  Gunnar Farnebäck,et al.  Fast and Accurate Motion Estimation Using Orientation Tensors and Parametric Motion Models , 2000, ICPR.

[11]  Maciej Smiatacz,et al.  AAM Toolkit: A System for Visual Object Appearance Modeling , 2010, MISSI.

[12]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[13]  Maciej Smiatacz,et al.  SDF classifier revisited , 2012, Expert Syst. J. Knowl. Eng..