Abstract
The unique features of human biometrics made it possible to benefit from these biometrics and use them as authentication methods. On the other hand, fraudulent biometrics are attempting to attack biometrics systems and are threatening the security of these systems. These risks can be avoided using liveness detection techniques, such as: heart rate measurement, pupil tracking, image quality assessment and many other techniques. Most face recognition systems use 2D cameras, where a 3D estimation of the face is derived as an antispoofing method. Not many systems are using 3D cameras for facial recognition, and therefore, its vulnerabilities to spoofing techniques are under-explored. In this paper, our purpose is to assess the 3D camera liveness assurance technique, and propose solutions that strengthens the gaps found in spoofing attack detection. Experiments will be conducted where we will use iFace 300 as a case study and attempt to attack the device using different attacking approaches such as mock face masks and 2D printed face images. We expect to discover weaknesses and strengths in the 3D liveness detection technique, and suggest methods to improve this technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hasan, T., Hansen, J.H.: A study on universal background model training in speaker verification. IEEE Trans. Audio, Speech Lang. Process. 19(7), 1890–1899 (2011)
Povey, D., Chu, S.M., Varadarajan, B.: Universal background model based speech recognition. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4561–4564. IEEE, New York (2008) [Online]. Available http://ieeexplore.ieee.org/document/4518671/
Reynolds, D.: Universal Background Models * Main Body Text
Ma, B., Li, C., Wang, Y., Zhang, Z., Huang, D.: Enhancing biometric security with wavelet quantization watermarking based two-stage multimodal authentication. In: International Conference on Pattern Recognition, no. ICPR, pp. 2416–2419 (2012)
Lasko, T.A., Bhagwat, J.G., Zou, K.H.: The use of receiver operating characteristic curves in biomedical informatics. J. Biomed. Inf. 38(5), 404–415 (2005) [Online]. Available http://www.sciencedirect.com/science/article/pii/S1532046405000171
Zou, K.H., Hall, W.J.: Two transformation models for estimating an ROC curve derived from continuous data. J. Appl. Stat. 27(5), 621–631 (2000)
Li, S.Z., Jain, A.K.: Encyclopedia of Biometrics. Springer, Berlin (2009) [Online]. Available http://www.springer.com/cn/book/9780387730035
Paderes, R.E.O.: A comparative review of biometric security systems. In: 2015 8th International Conference on Bio-Science and Bio-Technology (BSBT), pp. 8–11. IEEE, New York (2015) [Online]. Available http://ieeexplore.ieee.org/document/7433042/
Saha, R., Kundu, M., Dutta, M., Majumder, R., Mukherjee, D., Pramanik, S., Thakur, U.N., Mukherjee, C.: A Brief Study on Evolution of Iris Recognition System. In: Technology, Information (ed.) 2017 8th IEEE Annual Electronics and Mobile Communication Conference (IEMCON), pp. 685–688. IEEE, Vancouver, BC, Canada (2017)
Kanematsu, M., Takano, H., Nakamura, K.: Highly reliable liveness detection method for iris recognition. In: Proceedings of the SICE Annual Conference, pp. 361–364 (2007)
Daugman, J.: How iris recognition works. Ess.Tial Guid. Image Process. 14(1), 715–739 (2009)
Fingerprint Based Login to Ensure Patient Safety [Online]. Available https://www.bayometric.com/fingerprint-based-login-ensure-patient-safety/
M. S. A.-a. Mieee and H. A. Muhamad, “Effective Fingerprint Recognition Approach Based on Double Fingerprint Thumb,” pp. 75–80, 2017
Marcialis, G.L. et al.: First international fingerprint liveness detection competition-LivDet 2009. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5716 LNCS, pp. 12–23 (2009)
Yambay, D., Ghiani, L., Denti, P., Marcialis, G.L., Roli, F., Schuckers, S.: LivDet 2011 - Fingerprint liveness detection competition 2011. In: Proceedings - 2012 5th IAPR International Conference on Biometrics, ICB 2012, pp. 208–215 (2012)
Ghiani, L., Mura, V., Tocco, S., Marcialis, G.L., Roli, F., Yambay, D., Schuckers, S.: LivDet 2013 - Liveness Detection Competition 2013. In: Biometrics: Theory Applications and Systems (BTAS), pp. 0–5 (2013)
Matsumoto, T., Matsumoto, H., Yamada, K., Hoshino, S.: Impact of artificial “gummy” fingers on fingerprint systems. In: Proceedings of SPIE, vol. 4677(1), pp. 275–289 (2002)
Schuckers, S.A.C.: Spoofing and anti-spoofing measures 7(4), 56–62 (2002)
Drahansky, M.: Experiments with skin resistance and temperature for liveness detection. In: Proceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008, pp. 1075–1079 (2008)
Toosi, A., Bottino, A., Cumani, S., Negri, P., Sottile, P.L.: Feature Fusion for fingerprint liveness detection: a comparative study, pp. 23,695–23,709 (2017)
Kabir, W., Member, S., Ahmad, M.O., Swamy, M.N.S.: Palmprint recognition based on histograms of sparse codes, pp. 965–968 (2017)
Li, X., Chen, J., Zhao, G., Pietikäinen, M.: Remote heart rate measurement from face videos under realistic situations. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4264–4271 (2014)
Li, X., Komulainen, J., Zhao, G., Yuen, P.C., Pietikainen, M.: Generalized face anti-spoofing by detecting pulse from face videos. In: Proceedings - International Conference on Pattern Recognition, pp. 4244–4249 (2017)
Galbally, J., Marcel, S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2014) [Online]. Available http://ieeexplore.ieee.org/document/6671991/
Mhou, K., Van Der Haar, D., Leung, W.S.: Face spoof detection using light reflection in moderate to low lighting. In: 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2017, pp. 47–52 (2017)
Kittler, J., Hilton, A., Hamouz, M., Illingworth, J.: 3D assisted face recognition: a survey of 3D imaging, modelling and recognition approaches. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, vol. 3, pp. 114–120 (2005) [Online]. Available https://doi.org/10.1109/CVPR.2005.377 and http://ieeexplore.ieee.org/document/1565426/
A Look at How Snapchat’s Powerful Facial Recognition Tech Works. Accessed 04 Dec 2017 [Online]. Available https://petapixel.com/2016/06/30/snapchats-powerful-facial-recognition-technology-works/
Wasnik, P., Raja, K.B., Raghavendra, R., Busch, C.: Presentation attack detection in face biometric systems using raw sensor data from smartphones. In: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 104–111. IEEE, New York (2016) [Online]. Available http://ieeexplore.ieee.org/document/7907452/
Kim, Y., Yoo, J.-H.H., Choi, K.: A motion and similarity-based fake detection method for biometric face recognition systems. IEEE Trans. Consum. Electron. 57(2), 756–762 (2011) [Online]. Available http://ieeexplore.ieee.org/document/5955219/
Killiolu, M., Taşkiran, M., Kahraman, N.: Anti-spoofing in face recognition with liveness detection using pupil tracking. In: SAMI 2017 - IEEE 15th International Symposium on Applied Machine Intelligence and Informatics, Proceedings, pp. 87–92 (2017)
Yan, J., Zhang, Z., Lei, Z., Yi, D., Li, S.Z.: Face liveness detection by exploring multiple scenic clues. In: 2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, vol. 2012, December, pp. 188–193 (2012)
Kollreider, K., Fronthaler, H., Bigun, J.: Evaluating liveness by face images and the structure tensor. In: Proceedings - Fourth IEEE Workshop on Automatic Identification Advanced Technologies, AUTO ID 2005, vol. 2005, pp. 75–80 (2005)
Chan, P.P., Liu, W., Chen, D., Yeung, D.S., Zhang, F., Wang, X., Hsu, C C.: Face liveness detection using a flash against 2D spoofing attack. IEEE Trans. Inf. Forensics Secur. XX(X), pp. 1–14 (2017)
Lakshminarayana, N.N., Narayan, N., Napp, N., Setlur, S., Govindaraju, V.: A discriminative spatio-temporal mapping of face for liveness detection. In: 2017 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2017 (2017)
Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., Ho, A.T.: Detection of face spoofing using visual dynamics. IEEE Trans. Inf. Forensics Secur. 10(4), 762–777 (2015)
Aziz, A.Z.A., Wei, H., Ferryman, J.: Face anti-spoofing countermeasure: Efficient 2D materials classification using polarization imaging. In: Proceedings of 5th International Workshop on Biometrics and Forensics. IWBF 2017, 2017 (2017)
Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)
Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Face presentation attack detection by exploring spectral signatures. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2017-July, pp. 672–679 (2017)
Benlamoudi, A., Samai, D., Ouafi, A., Bekhouche, S.E., Taleb-Ahmed, A., Hadid, A.: Face spoofing detection using local binary patterns and Fisher Score. In: 3rd International Conference on Control, Engineering and Information Technology, CEIT 2015 (2015)
Mohan, K., Chandrasekhar, P., Jilani, S.: A combined HOG-LPQ with Fuz-SVM classifier for Object face Liveness Detection pp. 531–537 (2017)
Pravallika, P., Prasad, K.S.: SVM classification for fake biometric detection using image quality assessment: application to iris, face and palm print. In: 2016 International Conference on Inventive Computation Technologies (ICICT), pp. 1–6. IEEE, New York (2016) [Online]. Available http://ieeexplore.ieee.org/document/7823189/
Patil, A.A., Dhole, S.A.: Image Quality (IQ) based liveness detection system for multi-biometric detection. In: 2016 International Conference on Inventive Computation Technologies (ICICT), pp. 1–5. IEEE, New York (2016) [Online]. Available http://ieeexplore.ieee.org/document/7823297/
Karthik, K.: Image quality assessment based outlier detection for face anti-spoofing pp. 72–77 (2017)
Nguyen, H.P., Retraint, F., Morain-Nicolier, F., Delahaies, A.: Face spoofing attack detection based on the behavior of noises 63(1), 119–123 (2016)
Menotti, D., Chiachia, G., Pinto, A., Schwartz, W.R., Pedrini, H., Falcão, A.X., Rocha, A.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)
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)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forensics Secur. 11(8), 1818–1830 (2016)
Wang, T., Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection using 3D structure recovered from a single camera. In: Proceedings - 2013 International Conference on Biometrics, ICB 2013 (2013)
iFace Series User Manual (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Albakri, G., AlGhowinem, S. (2019). Investigating Spoofing Attacks for 3D Cameras Used in Face Biometrics. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-01057-7_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01056-0
Online ISBN: 978-3-030-01057-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)