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Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi

Year 2022, Volume: 15 Issue: 1, 56 - 65, 27.06.2022

Asuman Günay Yılmaz Uğur Turhal Vasif Nabiyev

https://doi.org/10.54525/tbbmd.1075383

Abstract

Yüz tanıma sistemleri temassız olmaları ve kullanım kolaylığından dolayı pek çok uygulamada kendine yer bulmaktadır. Fakat teknolojinin gelişimi ve bilgiye erişimin kolaylaşması nedeniyle bu sistemler, sahte yüzler kullanılarak yapılan saldırılara karşı dayanıksızdır. Bu çalışmada, farklı renk uzaylarındaki kanallardan çıkarılan doku özniteliklerinin yüz sahteciliği tespitindeki başarımı incelenmiştir. Bu amaçla HSV, YCbCr ve daha önceden bu alanda kullanılmayan L*a*b* renk uzaylarının kanallarından çıkarılan çok seviyeli yerel ikili örüntü özniteliklerinin çeşitli birleşimleri ile yüz sahtecilik tespiti gerçekleştirilmiştir. Öznitelik vektörleri temel bileşenler analizi ile küçültülüp, destek vektör makinesi sınıflayıcısının eğitiminde kullanılmıştır. CASIA ve Replay-Attack veri setleri üzerinde yapılan deneylerde farklı kanallardan çıkarılan öznitelik birleşimlerinin yüz sahteciliği tespitinde başarılı olduğu görülmüştür.

Keywords

Yüz sahteciliği tespiti renkli doku analizi yerel ikili örüntüler

References

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  • [2] Boulkenafet, Z., Komulainen, J., Hadid, A., Face anti-spoofing based on color texture analysis, Proceedings - International Conference on Image Processing, ICIP, 2015, pp. 2636–2640. https://doi.org/10.1109/ICIP.2015.7351280
  • [3] Parveen, S., Ahmad, S. M. S., Abbas, N. H., Adnan, W. A. W., Hanafi, M., Naeem, N., Face liveness detection using dynamic local ternary pattern, Computers, 5(2), 2016, pp. 1–15. https://doi.org/10.3390/computers5020010
  • [4] Zhang, L. B., Peng, F., Qin, L., Long, M., Face spoofing detection based on color texture Markov feature and support vector machine recursive feature elimination, Journal of Visual Communication and Image Representation, 51, 2018, pp. 56–69. https://doi.org/10.1016/j.jvcir.2018.01.001
  • [5] Boulkenafet, Z., Komulainen, J., Hadid, A., On the generalization of color texture-based face anti-spoofing, Image and Vision Computing, 77, 2018, pp.1–9.
  • [6] Li, L., Feng, X., Xia, Z., Jiang, X., Hadid, A., Face spoofing detection with local binary pattern network, Journal of Visual Communication and Image Representation, 54, 2018, pp.182–192. https://doi.org/10.1016/j.jvcir.2018.05.009
  • [7] Li, L., Feng, X., Jiang, X., Xia, Z., Hadid, A., Face anti-spoofing via deep local binary patterns, Proceedings - International Conference on Image Processing, ICIP, 2018, pp.101–105. https://doi.org/10.1109/ICIP.2017.8296251
  • [8] Li, H., He, P., Wang, S., Rocha, A., Jiang, X., Kot, A. C., Learning Generalized Deep Feature Representation for Face Anti-Spoofing, IEEE Transactions on Information Forensics and Security, 13(10), 2018, pp.2639–2652. https://doi.org/10.1109/TIFS.2018.2825949
  • [9] Chingovska, I., Anjos, A., Marcel, S., On the effectiveness of local binary patterns in face anti-spoofing, Proceedings of the International Conference of the Biometrics Special Interest Group, BIOSIG 2012, 2012.
  • [10] Määttä, J., Hadid, A., Pietikäinen, M., Face spoofing detection from single images using micro-texture analysis, International Joint Conference on Biometrics, IJCB, 2011. https://doi.org/10.1109/IJCB.2011.6117510
  • [11] Boulkenafet, Z., Komulainen, J., Hadid, A., Face Spoofing Detection Using Colour Texture Analysis, IEEE Transactions on Information Forensics and Security, 11(8), 2016, pp. 1818–1830. https://doi.org/10.1109/TIFS.2016.2555286
  • [12] Zhou, J., Shu, K., Liu, P., Xiang, J., Xiong, S., Face anti-spoofing based on dynamic color texture analysis using local directional number pattern, Proceedings - International Conference on Pattern Recognition, 2020, pp.4221–4228. https://doi.org/10.1109/ICPR48806.2021.9412323
  • [13] Anjos, A., Chakka, M. M., Marcel, S.,Motion-based counter-measures to photo attacks in face recognition, IET Biometrics, 3(3), 2014, pp.147–158. https://doi.org/10.1049/iet-bmt.2012.0071
  • [14] Alotaibi, A., Mahmood, A., Deep face liveness detection based on nonlinear diffusion using convolution neural network, Signal, Image and Video Processing, 11(4), 2017, pp.713–720. https://doi.org/10.1007/s11760-016-1014-2
  • [15] Galbally, J., Marcel, S., Fierrez, J., Image quality assessment for fake biometric detection: Application to Iris, fingerprint, and face recognition, IEEE Transactions on Image Processing, 23(2), 2014, pp.710–724. https://doi.org/10.1109/TIP.2013.2292332
  • [16] Wen, D., Han, H., Jain, A. K., Face spoof detection with image distortion analysis, IEEE Transactions on Information Forensics and Security, 2015. https://doi.org/10.1109/TIFS.2015.2400395
  • [17] Agarwal, A., Singh, R., Vatsa, M., Face anti-spoofing using Haralick features, IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS, 2016. https://doi.org/10.1109/BTAS.2016.7791171
  • [18] Määttä, J., Hadid, A., Pietikäinen, M., Face spoofing detection from single images using texture and local shape analysis, IET Biometrics, 1(1), 2012, pp.3–10. https://doi.org/10.1049/iet-bmt.2011.0009
  • [19] Patel, K., Han, H., Jain, A. K., Secure Face Unlock: Spoof Detection on Smartphones, IEEE Transactions on Information Forensics and Security, 11(10), 2016, pp.2268–2283. https://doi.org/10.1109/TIFS.2016.2578288
  • [20] Boulkenafet, Z., Komulainen, J., Hadid, A., Face antispoofing using speeded-up robust features and fisher vector encoding, IEEE Signal Processing Letters, 24(2), 2017, pp.141–145. https://doi.org/10.1109/LSP.2016.2630740
  • [21] Peng, F., Qin, L., Long, M., Face presentation attack detection using guided scale texture, Multimedia Tools and Applications 77(7), 2017 pp.8883–8909. https://doi.org/10.1007/S11042-017-4780-0
  • [22] Khurshid, A., Tamayo, S. C., Fernandes, E., Gadelha, M. R., Teofilo, M., A robust and real-time face anti-spoofing method based on texture feature analysis, International Conference on Human-Computer Interaction, 2019, pp.484–496.
  • [23] King, D. E., Dlib-ml: A machine learning toolkit. The Journal of Machine Learning Research, 10, 2009, pp.1755–1758.
  • [24] Bora, D. J., Kumar Gupta, A., Khan, F. A., Comparing the Performance of L*A*B** and HSV Color Spaces with Respect to Color Image Segmentation, International Journal of Emerging Technology and Advanced Engineering, 5 (2), 2015.
  • [25] Huang, Z. K., Liu, D. H., Segmentation of color image using EM algorithm in HSV color space, Proceedings of the 2007 International Conference on Information Acquisition, 2007, pp.316–319. https://doi.org/10.1109/ICIA.2007.4295749
  • [26] Vezhnevets, V., Sazonov, V., Andreeva, A., A Survey on Pixel-Based Skin Color Detection Technique, In Proceedings of the Graphi Conference, 2003, pp.85-92.
  • [27] Murali, S., Govindan, V. K., Shadow detection and removal from a single image: Using LAB color space, Cybernetics and Information Technologies, 13(1), 2013, pp.95–103. https://doi.org/10.2478/cait-2013-0009
  • [28] Baldevbhai, P. J., Anand, R. S., Color Image Segmentation for Medical Images using L*a*b** Color Space, IOSR Journal of Electronics and Communication Engineering, 1(2), 2012, pp.24–45. https://doi.org/10.9790/2834-0122445
  • [29] Ojala, T., Pietikäinen, M., Harwood, D., Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, Proceedings - International Conference on Pattern Recognition, 3, 1994, pp.582–585. https://doi.org/10.1109/ICPR.1994.576366
  • [30] Ojala, T., Pietikäinen, M., Mäenpää, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  • [31] Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S. Z., A face antispoofing database with diverse attacks, Proceedings - 2012 5th IAPR International Conference on Biometrics, 2012, pp.2–7. https://doi.org/10.1109/ICB.2012.6199754

Year 2022, Volume: 15 Issue: 1, 56 - 65, 27.06.2022

Asuman Günay Yılmaz Uğur Turhal Vasif Nabiyev

https://doi.org/10.54525/tbbmd.1075383

Abstract

References

  • [1] De Freitas Pereira, T., Komulainen, J., Anjos, A., De Martino, J. M., Hadid, A., Pietikäinen, M., Marcel, S., Face liveness detection using dynamic texture, EURASIP Journal on Image and Video Processing, 2, 2014, http://jivp.eurasipjournals.com/content/2014/1/2
  • [2] Boulkenafet, Z., Komulainen, J., Hadid, A., Face anti-spoofing based on color texture analysis, Proceedings - International Conference on Image Processing, ICIP, 2015, pp. 2636–2640. https://doi.org/10.1109/ICIP.2015.7351280
  • [3] Parveen, S., Ahmad, S. M. S., Abbas, N. H., Adnan, W. A. W., Hanafi, M., Naeem, N., Face liveness detection using dynamic local ternary pattern, Computers, 5(2), 2016, pp. 1–15. https://doi.org/10.3390/computers5020010
  • [4] Zhang, L. B., Peng, F., Qin, L., Long, M., Face spoofing detection based on color texture Markov feature and support vector machine recursive feature elimination, Journal of Visual Communication and Image Representation, 51, 2018, pp. 56–69. https://doi.org/10.1016/j.jvcir.2018.01.001
  • [5] Boulkenafet, Z., Komulainen, J., Hadid, A., On the generalization of color texture-based face anti-spoofing, Image and Vision Computing, 77, 2018, pp.1–9.
  • [6] Li, L., Feng, X., Xia, Z., Jiang, X., Hadid, A., Face spoofing detection with local binary pattern network, Journal of Visual Communication and Image Representation, 54, 2018, pp.182–192. https://doi.org/10.1016/j.jvcir.2018.05.009
  • [7] Li, L., Feng, X., Jiang, X., Xia, Z., Hadid, A., Face anti-spoofing via deep local binary patterns, Proceedings - International Conference on Image Processing, ICIP, 2018, pp.101–105. https://doi.org/10.1109/ICIP.2017.8296251
  • [8] Li, H., He, P., Wang, S., Rocha, A., Jiang, X., Kot, A. C., Learning Generalized Deep Feature Representation for Face Anti-Spoofing, IEEE Transactions on Information Forensics and Security, 13(10), 2018, pp.2639–2652. https://doi.org/10.1109/TIFS.2018.2825949
  • [9] Chingovska, I., Anjos, A., Marcel, S., On the effectiveness of local binary patterns in face anti-spoofing, Proceedings of the International Conference of the Biometrics Special Interest Group, BIOSIG 2012, 2012.
  • [10] Määttä, J., Hadid, A., Pietikäinen, M., Face spoofing detection from single images using micro-texture analysis, International Joint Conference on Biometrics, IJCB, 2011. https://doi.org/10.1109/IJCB.2011.6117510
  • [11] Boulkenafet, Z., Komulainen, J., Hadid, A., Face Spoofing Detection Using Colour Texture Analysis, IEEE Transactions on Information Forensics and Security, 11(8), 2016, pp. 1818–1830. https://doi.org/10.1109/TIFS.2016.2555286
  • [12] Zhou, J., Shu, K., Liu, P., Xiang, J., Xiong, S., Face anti-spoofing based on dynamic color texture analysis using local directional number pattern, Proceedings - International Conference on Pattern Recognition, 2020, pp.4221–4228. https://doi.org/10.1109/ICPR48806.2021.9412323
  • [13] Anjos, A., Chakka, M. M., Marcel, S.,Motion-based counter-measures to photo attacks in face recognition, IET Biometrics, 3(3), 2014, pp.147–158. https://doi.org/10.1049/iet-bmt.2012.0071
  • [14] Alotaibi, A., Mahmood, A., Deep face liveness detection based on nonlinear diffusion using convolution neural network, Signal, Image and Video Processing, 11(4), 2017, pp.713–720. https://doi.org/10.1007/s11760-016-1014-2
  • [15] Galbally, J., Marcel, S., Fierrez, J., Image quality assessment for fake biometric detection: Application to Iris, fingerprint, and face recognition, IEEE Transactions on Image Processing, 23(2), 2014, pp.710–724. https://doi.org/10.1109/TIP.2013.2292332
  • [16] Wen, D., Han, H., Jain, A. K., Face spoof detection with image distortion analysis, IEEE Transactions on Information Forensics and Security, 2015. https://doi.org/10.1109/TIFS.2015.2400395
  • [17] Agarwal, A., Singh, R., Vatsa, M., Face anti-spoofing using Haralick features, IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS, 2016. https://doi.org/10.1109/BTAS.2016.7791171
  • [18] Määttä, J., Hadid, A., Pietikäinen, M., Face spoofing detection from single images using texture and local shape analysis, IET Biometrics, 1(1), 2012, pp.3–10. https://doi.org/10.1049/iet-bmt.2011.0009
  • [19] Patel, K., Han, H., Jain, A. K., Secure Face Unlock: Spoof Detection on Smartphones, IEEE Transactions on Information Forensics and Security, 11(10), 2016, pp.2268–2283. https://doi.org/10.1109/TIFS.2016.2578288
  • [20] Boulkenafet, Z., Komulainen, J., Hadid, A., Face antispoofing using speeded-up robust features and fisher vector encoding, IEEE Signal Processing Letters, 24(2), 2017, pp.141–145. https://doi.org/10.1109/LSP.2016.2630740
  • [21] Peng, F., Qin, L., Long, M., Face presentation attack detection using guided scale texture, Multimedia Tools and Applications 77(7), 2017 pp.8883–8909. https://doi.org/10.1007/S11042-017-4780-0
  • [22] Khurshid, A., Tamayo, S. C., Fernandes, E., Gadelha, M. R., Teofilo, M., A robust and real-time face anti-spoofing method based on texture feature analysis, International Conference on Human-Computer Interaction, 2019, pp.484–496.
  • [23] King, D. E., Dlib-ml: A machine learning toolkit. The Journal of Machine Learning Research, 10, 2009, pp.1755–1758.
  • [24] Bora, D. J., Kumar Gupta, A., Khan, F. A., Comparing the Performance of L*A*B** and HSV Color Spaces with Respect to Color Image Segmentation, International Journal of Emerging Technology and Advanced Engineering, 5 (2), 2015.
  • [25] Huang, Z. K., Liu, D. H., Segmentation of color image using EM algorithm in HSV color space, Proceedings of the 2007 International Conference on Information Acquisition, 2007, pp.316–319. https://doi.org/10.1109/ICIA.2007.4295749
  • [26] Vezhnevets, V., Sazonov, V., Andreeva, A., A Survey on Pixel-Based Skin Color Detection Technique, In Proceedings of the Graphi Conference, 2003, pp.85-92.
  • [27] Murali, S., Govindan, V. K., Shadow detection and removal from a single image: Using LAB color space, Cybernetics and Information Technologies, 13(1), 2013, pp.95–103. https://doi.org/10.2478/cait-2013-0009
  • [28] Baldevbhai, P. J., Anand, R. S., Color Image Segmentation for Medical Images using L*a*b** Color Space, IOSR Journal of Electronics and Communication Engineering, 1(2), 2012, pp.24–45. https://doi.org/10.9790/2834-0122445
  • [29] Ojala, T., Pietikäinen, M., Harwood, D., Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, Proceedings - International Conference on Pattern Recognition, 3, 1994, pp.582–585. https://doi.org/10.1109/ICPR.1994.576366
  • [30] Ojala, T., Pietikäinen, M., Mäenpää, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  • [31] Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S. Z., A face antispoofing database with diverse attacks, Proceedings - 2012 5th IAPR International Conference on Biometrics, 2012, pp.2–7. https://doi.org/10.1109/ICB.2012.6199754
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler(Araştırma)
Authors

Asuman Günay Yılmaz 0000-0003-3960-5085

Uğur Turhal 0000-0002-5627-1833

Vasif Nabiyev 0000-0003-0314-8134

Early Pub Date June 27, 2022
Publication Date June 27, 2022
Published in Issue Year 2022 Volume: 15 Issue: 1

Cite

APA Günay Yılmaz, A., Turhal, U., & Nabiyev, V. (2022). Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 15(1), 56-65. https://doi.org/10.54525/tbbmd.1075383
AMA Günay Yılmaz A, Turhal U, Nabiyev V. Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi. TBV-BBMD. June 2022;15(1):56-65. doi:10.54525/tbbmd.1075383
Chicago Günay Yılmaz, Asuman, Uğur Turhal, and Vasif Nabiyev. “Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 15, no. 1 (June 2022): 56-65. https://doi.org/10.54525/tbbmd.1075383.
EndNote Günay Yılmaz A, Turhal U, Nabiyev V (June 1, 2022) Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 15 1 56–65.
IEEE A. Günay Yılmaz, U. Turhal, and V. Nabiyev, “Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi”, TBV-BBMD, vol. 15, no. 1, pp. 56–65, 2022, doi: 10.54525/tbbmd.1075383.
ISNAD Günay Yılmaz, Asuman et al. “Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 15/1 (June 2022), 56-65. https://doi.org/10.54525/tbbmd.1075383.
JAMA Günay Yılmaz A, Turhal U, Nabiyev V. Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi. TBV-BBMD. 2022;15:56–65.
MLA Günay Yılmaz, Asuman et al. “Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, vol. 15, no. 1, 2022, pp. 56-65, doi:10.54525/tbbmd.1075383.
Vancouver Günay Yılmaz A, Turhal U, Nabiyev V. Farklı Renk Kanallarında Üretilen Doku Özniteliklerinin Yüz Sahteciliği Tespiti Başarımına Etkisinin İncelenmesi. TBV-BBMD. 2022;15(1):56-65.
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