Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Multi-purpose Image Counter-anti-forensic Method Using Convolutional Neural Networks Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Detection of Video-Based Face Spoofing Using LBP and Multiscale DCT

verfasst von : Ye Tian, Shijun Xiang

Erschienen in: Digital Forensics and Watermarking

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Despite the great deal of progress during the recent years, face spoofing detection is still a focus of attention. In this paper, an effective, simple and time-saving countermeasure against video-based face spoofing attacks based on LBP (Local Binary Patterns) and multiscale DCT (Discrete Cosine Transform) is proposed. Adopted as the low-level descriptors, LBP features are used to extract spatial information in each selected frame. Next, multiscale DCT is performed along the ordinate axis of the obtained LBP features to extract spatial information. Representing both spatial and temporal information, the obtained high-level descriptors (LBP-MDCT features) are finally fed into a SVM (Support Vector Machine) classifier to determine whether the input video is a facial attack or valid access. Compared with state of the art, the excellent experimental results of the proposed method on two benchmarking datasets (Replay-Attack and CASIA-FASD dataset) have demonstrated its effectiveness.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Multi-purpose Image Counter-anti-forensic Method Using Convolutional Neural Networks
Nächstes Kapitel Source Cell-Phone Identification Using Spectral Features of Device Self-noise
Literatur
1.
Galbally, J., Marcel, S., Fierrez, J.: Biometric antispoofing methods: a survey in face recognition. IEEE Access 2, 1530–1552 (2014)CrossRef
2.
Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: 2011 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE Press, Washington, DC (2011)
3.
Galbally, J., Fierrez, J., Alonso-Fernandez, F., Martinez-Diaz, M.: Evaluation of direct attacks to fingerprint verification systems. Telecommun. Syst. 47(3–4), 243–254 (2011)CrossRef
4.
Mjaaland, B.B., Bours, P., Gligoroski, D.: Walk the walk: attacking gait biometrics by imitation. In: Burmester, M., Tsudik, G., Magliveras, S., Ilić, I. (eds.) ISC 2010. LNCS, vol. 6531, pp. 361–380. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-18178-8_​31 CrossRef
5.
Chen, H., Valizadegan, H., Jackson, C., Soltysiak, S., Jain, A.K.: Fake hands: spoofing hand geometry systems. In: 2005 Biometrics Consortium Conference (BCC) (2005)
6.
Bin, Q., Jian-Fei, P., Guang-Zhong, C., Ge-Guo, D.: The anti-spoofing study of vein identification system. In: International Conference on Computational Intelligence and Security (ICCIS), pp. 357–360 (2009)
7.
Akhtar, Z., Fumera, G., Marcialis, G.L., Roli, F.: Evaluation of serial and parallel multibiometric systems under spoofing attacks. In: 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 283–288 (2012)
8.
Tome, P., Vanoni, M., Marcel, S.: On the vulnerability of finger vein recognition to spoofing. In: International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–10 (2014)
9.
Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 504–517. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-15567-3_​37 CrossRef
10.
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.: A face antispoofing database with diverse attacks. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 26–31. IEEE Press (2012)
11.
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: International Conference of Biometrics Special Interest Group (BIOSIG), pp. 1–7. IEEE Press, Darmstadt (2012)
12.
Erdogmus, N., Marcel, S.: Spoofing in 2D face recognition with 3D masks and anti-spoofing with kinect. In: 6th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6 (2013)
13.
Pinto, A., Schwartz, W.R., Pedrini, H., de Rezende Rocha, A.: Using visual rhythms for detecting video-based facial spoof attacks. IEEE Trans. Inf. Forensics Secur. 10(5), 1025–1038 (2015)CrossRef
14.
Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. Proc. SPIE 5404, 296–303 (2004)CrossRef
15.
Peixoto, B., Michelassi, C., Rocha, A.: Face liveness detection under bad illumination conditions. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 3557–3560. IEEE Press, Brussels (2011)
16.
Maatta, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using texture and local shape analysis. IET Biometrics 1(1), 3–10 (2012)CrossRef
17.
Kose, N., Dugelay, J.L.: Classification of captured and recaptured images to detect photograph spoofing. In: 2012 International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, pp. 1027–1032 (2012)
18.
Maatta, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE Press, Washington, DC (2011)
19.
Nguyen, H.H., Nguyen-Son, H.-Q., Nguyen, T.D., Echizen, I.: Discriminating between computer-generated facial images and natural ones using smoothness property and local entropy. In: Shi, Y.-Q., Kim, H.J., Pérez-González, F., Echizen, I. (eds.) IWDW 2015. LNCS, vol. 9569, pp. 39–50. Springer, Heidelberg (2016). doi:10.​1007/​978-3-319-31960-5_​4 CrossRef
20.
de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: LBP–TOP based countermeasure against face spoofing attacks. In: Park, J.-I., Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-37410-4_​11 CrossRef
21.
Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., Ho, A.T.S.: Detection of face spoofing using visual dynamics. IEEE Trans. Inf. Forensics Secur. 10(4), 762–777 (2015)CrossRef
22.
Arashloo, S.R., Kittler, J., Christmas, W.: Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features. IEEE Trans. Inf. Forensics Secur. 10(11), 2396–2407 (2015)CrossRef
23.
Pinto, A., Pedrini, H., Schwartz, W.R., Rocha, A.: Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Trans. Image Process. 24(12), 4726–4740 (2015)MathSciNetCrossRef
24.
da Silva Pinto, A., Pedrini, H., Schwartz, W., Rocha, A.: Video-based face spoofing detection through visual rhythm analysis. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, Ouro Preto, pp. 221–228 (2012)
25.
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRef
26.
Arashloo, S.R., Kittler, J.: Class-specific kernel fusion of multiple descriptors for face verification using multiscale binarised statistical image features. IEEE Trans. Inf. Forensics Secur. 9(12), 2100–2109 (2014)CrossRef
27.
Arashloo, S.R., Kittler, J.: Dynamic texture recognition using multiscale binarized statistical image features. IEEE Trans. Multimedia 16(8), 2099–2109 (2014)CrossRef
28.
Chan, C.H., Tahir, M.A., Kittler, J., Pietikainen, M.: Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1164–1177 (2013)CrossRef
29.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATH
30.
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 J. Image Video Process. 2014(2), 1–15 (2014)
Metadaten
Titel
Detection of Video-Based Face Spoofing Using LBP and Multiscale DCT
verfasst von
Ye Tian
Shijun Xiang
Copyright-Jahr
2017
Buch
Digital Forensics and Watermarking

Print ISBN: 978-3-319-53464-0

Electronic ISBN: 978-3-319-53465-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-53465-7_2

Premium Partner

    Bildnachweise