Abstract
Biometrics is an emerging research area due to its easiness in identification of the person. Face Spoofing is the challenging task in face recognition systems because the human can easily trickster the system by presenting the video or photograph of the person. Many approaches are providing good results in face spoofing, but still it is challenging in intra and cross database validation. Deep learning algorithms have shown significant results in the intra and cross database. This paper used deep learning for extracting the inclusive and favorable features of the person from the face. The extracted features are used for classifying the face image as a real face or genuine face. The performance of the method is evaluated through statistical measures. The experiments were carried out NUAA and CASIA database. The method attained most promising results than other face spoofing methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Bashier, H.K., Lau, S.H., Han, P.Y., Ping, L.Y., Li, C.M.: Face spoofing detection using local graph structure. In: International Conference on Computer, Communications and Information Technology (2014)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forensics Secur. 11(8), 1818–1830 (2016)
Candemir, S., Borovikov, E., Santosh, K.C., Antani, S.K., Thoma, G.R.: RSILC: rotation- and scale-invariant, line-based color-aware descriptor. Image Vis. Comput. 42, 1–12 (2015). https://doi.org/10.1016/j.imavis.2015.06.010
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: International Conference of the Biometrics Special Interest Group, pp. 1–7 (2012)
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). https://doi.org/10.1007/978-3-642-37410-4_11
Housam, K.B., Lau, S.H., Pang, Y.H., Liew, Y.P., Chiang, M.L.: Face spoofing detection based on improved local graph structure. In: International Conference on Information Science & Applications, pp. 1–4 (2014)
Jabid, T., Kabir, M.H., Chae, O.: Local directional pattern (LDP) for face recognition. In: IEEE International Conference on Consumer Electronics, pp. 329–330 (2010)
Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In: Defense and Security, pp. 296–303 (2004)
Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using texture and local shape analysis. IET Biom. 1(1), 3–10 (2012)
Menotti, D., et al.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)
Peixoto, B., Michelassi, C., Rocha, A.: Face liveness detection under bad illumination conditions. In: 18th IEEE International Conference on Image Processing, pp. 3557–3560 (2011)
Srinivasa Perumal, R., Chandra Mouli, P.V.S.S.R.: Dimensionality reduced local directional pattern (DR-LDP) for face recognition. Expert Syst. Appl. 63, 66–73 (2016)
Rehman, Y.A.U., Po, L.M., Liu, M.: Livenet: improving features generalization for face liveness detection using convolution neural networks. Expert Syst. Appl. 108, 159–169 (2018)
Santosh, K.C., Lamiroy, B., Wendling, L.: Integrating vocabulary clustering with spatial relations for symbol recognition. IJDAR 17(1), 61–78 (2014). https://doi.org/10.1007/s10032-013-0205-4
Sawat, D.D., Hegadi, R.S.: Lower facial curves extraction for unconstrained face detection in video. In: Bera, R., Sarkar, S.K., Chakraborty, S. (eds.) Advances in Communication, Devices and Networking. LNEE, vol. 462, pp. 689–700. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7901-6_75
de Souza, G.B., da Silva Santos, D.F., Pires, R.G., Marana, A.N., Papa, J.P.: Deep texture features for robust face spoofing detection. IEEE Trans. Circuits Syst. II Express Briefs 64(12), 1397–1401 (2017)
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). https://doi.org/10.1007/978-3-642-15567-3_37
Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: 5th IAPR International Conference on Biometrics (ICB), pp. 26–31 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Srinivasa Perumal, R., Santosh, K.C., Chandra Mouli, P.V.S.S.R. (2019). Learning Deep Feature Representation for Face Spoofing. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_16
Download citation
DOI: https://doi.org/10.1007/978-981-13-9181-1_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9180-4
Online ISBN: 978-981-13-9181-1
eBook Packages: Computer ScienceComputer Science (R0)