Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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 presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
257--268
Opis fizyczny
Bibliogr. 17 poz., rys., wykr., wzory
Twórcy
autor
- Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Department of Intelligent Interactive Systems, Narutowicza 11/12, 80-233 Gdansk, Poland, slowhand@eti.pg.gda.pl
Bibliografia
- [1] Researchers spoof, bypass face-recognition authentication systems, Homeland Security NewsWire (2009). http://www.homelandsecuritynewswire.com/researchers-spoof-bypass-face-recognition-authenticationsystems.
- [2] SART-2, Biometric security system for mobile workstations (2012). http://sart2.eti.pg.gda.pl/en/.
- [3] Jee, H., Jung, S., Yoo, J. (2006). Liveness Detection for Embedded Face Recognition System. International Journal of Biomedical Sciences, 235-238.
- [4] Viola, P., Jones, M.J., (2004). Robust Real-Time Face Detection. Int. J. Comp. Vision, 57(2), 137-154.
- [5] Wang, H., Li, S.Z., Wang, Y. (2004). Face Recognition under Varying Lighting Conditions Using Self Quotient Image. Proc. 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition (FGR’04).
- [6] Pan, G., Sun, L., Wu, Z. (2007). Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera. Proc. IEEE 11th Int. Conf. on Computer Vision (ICCV 2007).
- [7] Huang, C.-H., Wang, J.-F. (2008). SVM-based One-Against-Many Algorithm for Liveness Face Authentication. Proc. IEEE 11th Int. Conf. on Systems, Man and Cybernetics (SMC 2008), 744-748.
- [8] Bao, W., Li, H., Li, N., Jiang, W. (2009). A Liveness Detection Method for Face Recognition Based on Optical Flow Field. Proc. Int. Conf. on Image Analysis and Signal Processing (IASP 2009), 233-236.
- [9] Dembski, J., Smiatacz, M. (2010). Modular machine learning system for training object detection algorithms on a supercomputer. Advances in Systems Science, Academic Publishing House EXIT, 353-361.
- [10] Horn, B., Schunk, B. (1981). Determining Optical Flow. Artificial Intelligence, 17, 185-204.
- [11] Farnebäck, G. (2000). Fast and Accurate Motion Estimation using Orientation Tensors and Parametric Motion Models. Proc. Int. Conf. on Pattern Recognition (ICPR 2000).
- [12] Farnebäck, G. (1999). Spatial Domain Methods for Orientation and Velocity Estimation. Lic. Thesis LiUTek-Lic-1999:13. Dept. EE, Linköping University.
- [13] Burges, Ch.J.C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2), 121-167.
- [14] Cui, J., Wang, Y. (2011). Analog circuit fault classification using improved one-against-one Support Vector Machines. Metrol. Meas. Sys., 18(4), 569-582.
- [15] Smiatacz, M., Malina, W. (2011). SDF classifier revisited. Expert Systems. DOI: 10.1111/j.1468-0394.2011.00589.x
- [16] Beauty check, Average faces (2012). http://www.uni-regensburg.de/Fakultaeten/phil_Fak_II/Psychologie/Psy_II/beautycheck/english/durchschnittsgesichter/durchschnittsgesichter.htm.
- [17] Smiatacz, M., Sikora D. (2010). AAM Toolkit: a system for visual object appearance modelling. Advances in Multimedia and Network Information System Technologies, Advances in Intelligent and Soft Computing, 80, 121-129.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0097-0007