Cover page and Table of Contents: PDF (size: 493KB)
Full Text (PDF, 493KB), PP.15-20
Views: 0 Downloads: 0
Spoof detection, handcrafted texture extraction, convolutional neural network, decision level fusion, score level fusion
Recognition performance of biometric systems is affected through spoofing attacks made by fake identities. The focus of this paper is on presenting a new scheme based on score level and decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involve consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this approach, convolutional neural network (CNN) and overlapped histograms of local binary patterns (OVLBP) methods is used to extract facial features of images. The produced matching scores provided by CNN and OVLBP then combined to form a fused score vector. Finally, the last decision on real and attack images is done by combining decisions of hybrid scheme using majority vote of CNN, OVLBP and their fused vector. Experimental results on public spoof databases such as Print-Attack and Replay-Attack face databases demonstrate the strength of the proposed anti-spoofing method for fake detection.
Omid. Sharifi, "Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.2, pp. 15-20, 2019. DOI: 10.5815/ijigsp.2019.02.02
A.K Jain,.; A.Ross,; Prabhakar, S. An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 2004, 14, 4–20.
A. K., Jain, S. Z Li,. Handbook of face recognition. New York: springer, 2011.
M. Ç. Yildiz, O.Sharifi, and M. Eskandari, Log-Gabor Transforms and Score Fusion to Overcome Variations in Appearance for Face Recognition, International Conference on Computer Vision and Graphics, – Proceedings of International Conference on Computer Vision and Graphics, ICCVG 2016, Warsaw, Poland, September 19-21, 2016. Springer 2016 Lecture Notes in Computer Science.
H. S. Bhatt., S. Bharadwaj, R. Singh,& M, Vatsa. Recognizing surgically altered face images using multiobjective evolutionary algorithm. IEEE Transactions on Information Forensics and Security, 2013, 8(1), 89-100.
O. Sharifi, M. Eskandari, and M. Ç Yildiz., Scheming an Efficient Facial Recognition System using Global and Random Local Feature Extraction Methods, 2nd International Conference on Computer Science and Engineering UBMK’17, October 5-8, 2017, Antalya, Turkey, DOI: 10.1109/UBMK.2017.8093508.
D. Zhang, Z. Guo, G. Lu,.; L. Zhang,; Y. Liu,; W. Zuo, Online joint palmprint and palmvein verification. Expert Syst. Appl. 2011, 38, 2621–2631.
M. Eskandari, O. Sharifi. Optimum scheme selection for face–iris biometric. IET Biometrics, 2016, 6(5), 334-341.
K. Nguyen, C. Fookes,; R. Jillela,; S. Sridharan,; A. Ross,. Long range iris recognition: A survey. Pattern Recognit. 2017, 72, 123–143.
O. Sharifi, M. Eskandari, Optimal Face-Iris Multimodal Fusion Scheme. Symmetry, 2016, 8(6), 48.
D.T Pham, Y.H. Park, D.T Nguyen, S.Y. Kwon,; K.R Park,. Nonintrusive finger-vein recognition system using NIR image sensor and accuracy analyses according to various factors. Sensors 2015, 15, 16866–16894.
D.T. Nguyen, H.S. Yoon, D.T. Pham,; K.R Park,. Spoof detection for finger-vein recognition system using NIR camera. Sensors 2017, 17, 2261.
D. Menotti, G. Chiachia,; A. Pinto, W.R Schwartz, H. Pedrini,; A.X. Falcao,; A. Rocha, Deep representation for iris, face and fingerprint spoofing detection. IEEE Trans. Inf. Forensic Secur. 2015, 10, 864–879.
K.R. Nalini, H.C. Jonathan, M.B. Ruud,: An analysis of minutiae matching strength. In: Audio- and Video-Based Biometric Person Authentication, Proceedings of 3rd AVBPA ed.,2001, vol. 2091, pp. 223–228.
M. JMarcos, F. Julian, , et al.: An evaluation of indirect attacks and countermeasures in fingerprint verification systems. Pattern Recognit. Lett. 2011, 32(12), 1643–1651.
T. Santosh, P. Norman, et al.: Detection of face spoofing using visual dynamics. IEEE Trans. Inf. Forensics Secur. 2015, 10(4), 762–777.
M., Eskandari, & O.Sharifi, Designing Efficient Spoof Detection Scheme for Face Biometric. In International Conference on Image and Signal Processing, 2018, July; pp. 427-434. Springer, Cham.
A. Anjos, M.M. Chakka, S. Marcel,: Countermeasures to photo attacks in face recognition. Biom. IET, 2014, 3(3), 147–158.
P. Gupta, et al. "On iris spoofing using print attack." Pattern Recognition (ICPR), 2014 22nd International Conference on. IEEE, 2014.
H. Abdenour, G. Mohammad, et al.: Can gait biometrics be spoofed. In: 2012 21st International Conference on Pattern Recognition.
B. Biggio, Z. Akhtar, G. Fumera, , G. L Marcialis., & F. Roli,. Security evaluation of biometric authentication systems under real spoofing attacks. IET biometrics, 2012, 1(1), 11-24.
M. Gomez-Barrero, G. Javier, and F. Julian. "Efficient software attack to multimodal biometric systems and its application to face and iris fusion." Pattern Recognition Letters 36, 2014: 243-253.
A., Zahid, S. Kale, and N. Alfarid. "Spoof attacks on multimodal biometric systems." Proc. International Conference on Information and Network Technology (IPCSIT). Vol. 4. 2011.
M.M. Chakka, A. Anjos, et al.: Competition on counter measures to 2D facial spoofing attacks. In: 2011 International Joint Conference on Biometrics.
K. Kollreider, H. Fronthaler, J. Bigun, Evaluating liveness by face images and the structure tensor. In: Automatic Identification Advanced Technologies, 2005.
K. Kollreider, H. Fronthaler, J. Bigun, Verifying Liveness By Multiple Experts In Face Biometrics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008.
A. Krizhevsky, I. Sutskever, G.E Hinton, ImageNet classification with deep convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–8 December 2012.
P.N. Druzhkov , V.D. Kustikova , A survey of deep learning methods and soft-ware tools for image classification and object detection, Pattern Recognit. Im- age Anal. 26 (1) (2016) 9–15.
Z. Guo, D. Zhang, D. Zhang, A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19(6) (June 2010) 1657-1663.
Print Attack face database, https://www.idiap.ch/dataset/printattack, Accessed October 2014.
Replay Attack face database, https://www.idiap.ch/dataset/replayattack, Accessed October 2014.
J. Galbally, M. Sébastien and J. Fierrez, "Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition." IEEE transactions on image processing 23.2 (2014): 710-724.
Samarth, et al. "Computationally efficient face spoofing detection with motion magnification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2013.
J. Määttä, H. Abdenour, and P. Matti. "Face spoofing detection from single images using micro-texture analysis." Biometrics (IJCB), 2011 international joint conference on. IEEE, 2011.
P. de Freitas, Tiago, et al. "Face liveness detection using dynamic texture." EURASIP Journal on Image and Video Processing 2014.1 (2014): 1-15.
D. Wen, H. Hu, and A. K. Jain. "Face spoof detection with image distortion analysis." IEEE Transactions on Information Forensics and Security 10.4 (2015): 746-761.
D. Nguyen, Tien, et al. "Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors." Sensors 18.3 (2018): 699.
T. Ojala, M. Pietikäinen, D. Harwood: A comparative study of texture measure with classification based on feature distributions. Pattern Recognit. 29, 51–59.
A. Benlamoudi; D. Samai.; A. Ouafi.; S.E Bekhouche, A. Taleb-Ahmed, Hadid, Face spoofing detection using local binary patterns and Fisher score. In Proceedings of the 3rd International Conference on Control
K. Simonyan; A. Zisserman, Very deep convolutional neural networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations, Kunming, China, 25–27 September 2013.
A. Pinto, et al. "Using visual rhythms for detecting video-based facial spoof attacks." IEEE Transactions on Information Forensics and Security 10.5 (2015): 1025-1038G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955.