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

Indra Bayu Kusuma, Arida Kartika, Tjokorda Agung Budi W, Kurniawan Nur Ramadhani, Febryanti Sthevanie

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.

Full Text:

PDF

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

83 0.835 0.84 0.845 0.85 0.855 0.86 0.865 0.87 0.875

LBPV8,2+Histeq LBPV8,4+Histeq LBPV8,5+Histeq LBPV8,7+Histeq

INTL. JOURNAL ON ICT VOL. XX, NO. XX, JUNE 2016 9

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.




DOI: http://dx.doi.org/10.21108/IJOICT.2018.42.134

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Indra Bayu Kusuma, Arida Kartika, Tjokorda Agung Budi W, Kurniawan Nur Ramadhani, Febryanti Sthevanie

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.