Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance

  • Indra Bayu Kusuma Telkom University
  • Arida Kartika Telkom Univesity
  • Tjokorda Agung Budi W Telkom University
  • Kurniawan Nur Ramadhani Telkom University
  • Febryanti Sthevanie Telkom University
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Abstract

Particularly in the field of biometric security using human face has been widely implemented in the real world. Currently the human face is one of the guidelines in the security system. Nowadays the challenge is how to detect data falsification; such an attack is called spoofing. Spoofing occurs when someone is trying to pretend to be someone else by falsifying the original data and then that person may gain illegal access and benefit him. For example one can falsify the face recognition system using photographs, video, masks or 3D models. In this paper image spoofing human face detection using texture analysis on input image is proposed. Texture analysis used in this paper is the Local Binary Pattern (LBP) and Local Binary Pattern Variance (LBPV). To classified input as original or spoof K-Nearest Neighbor (KNN) used. Experiment used 5761 spoofs and 3362 original from NUAA Imposter dataset. The experimental result yielded a best success rate of 87.22% in term of accuracy with configuration of the system using LBPV and histogram equalization with ratio 𝑅 = 7 and 𝑃 = 8.

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References

Maatta, J., Hadid, A., & Pietikainen, M. (2012). Face spoofing detection from single images using texture and local shape analysis. Biometrics, IET, 1(1), 3-10. [2] X. Tan, Y. Li, J. Liu, L. Jiang, “Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Mode,” in Proc. Of the 11th European conference on Computer vision,2010,pp. 504-517. [3] Komulainen, J., Hadid, A., & Pietikäinen, M. (2012, November). Face spoofing detection using dynamic texture. In Computer Vision-ACCV 2012 Workshops(pp. 146-157). Springer Berlin Heidelberg. [4] Määttä, J., Hadid, A., & Pietikainen, M. (2011, October). Face spoofing detection from single images using micro-texture

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analysis. In Biometrics (IJCB), 2011 international joint conference on (pp. 1-7). IEEE. [5] Wen, D., Han, H., & Jain, A. K. (2015). Face spoof detection with image distortion analysis. Information Forensics and Security, IEEE Transactions on,10(4), 746-761. [6] de Freitas Pereira, T., Anjos, A., De Martino, J. M., & Marcel, S. (2012, November). LBP− TOP based countermeasure against face spoofing attacks. In Computer Vision-ACCV 2012 Workshops (pp. 121-132). Springer Berlin Heidelberg. [7] C., Padraig, S.J., Delany. (2007, March). k-Nearest Neighbour Classifier. Technical Report UCD-CSI-2007-4. [8] Kose, N., & Dugelay, J.,-L. Classification of Captured and Recaptured Images to Detect Photograph Spoofing.Multi Media Department, EURECOM 2229. [9] Lahdenoja, O., Poikonen, J., & Laiho, M. (2013). Towards understanding the formation of uniform local binary patterns. ISRN Machine Vision, 2013. [10] Schuckers, S. A. (2002). Spoofing and anti-spoofing measures. Information Security technical report, 7(4), 56-62.

Published
2019-04-02
How to Cite
Kusuma, I. B., Kartika, A., Budi W, T. A., Ramadhani, K. N., & Sthevanie, F. (2019). Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance. International Journal on Information and Communication Technology (IJoICT), 4(2), 11-18. https://doi.org/10.21108/IJOICT.2018.42.134
Section
Security & Cryptography