Presentation Attack Detection for Face Recognition on Smartphones: A Comprehensive Review

Authors

  • Idris Abdul Ghaffar Embedded Computing Systems (EmbCos) Research Group, Biomedical Modelling and Simulation (BIOMEMS) Research Group, Faculty of Electrical and Electronics Engineering, University Tun Hussien Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Mohd Norzali Haji Mohd Embedded Computing Systems (EmbCos) Research Group, Biomedical Modelling and Simulation (BIOMEMS) Research Group, Faculty of Electrical and Electronics Engineering, University Tun Hussien Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

Keywords:

Anti-spoofing, Face Recognition, Presentation Attack Countermeasures, Presentation Attack Detection,

Abstract

Even though the field of Face Presentation Attack Detection (PAD) has been around for quite a long time, but still it is quite a new field to be implemented on smartphones. Implementation on smartphones is different because the limited computing power of the smartphones when compared to computers. Presentation Attack for a face recognition system may happen in various ways, using photograph, video or mask of an authentic user’s face. The Presentation Attack Detection system is vital to counter those kinds of intrusion. Face presentation attack countermeasures are categorized as sensor level or feature level. Face Presentation Attack Detection through the sensor level technique involved in using additional hardware or sensor to protect recognition system from spoofing while feature level techniques are purely software-based algorithms and analysis. Under the feature level techniques, it may be divided into liveness detection; motion analysis; face appearance properties (texture analysis, reflectance); image quality analysis (image distortion); contextual information; challenge response. There are a few types of research have been done for face PAD on smartphones. They also have released the database they used for their testing and performance benchmarking.

References

Y. Li, K. Xu, Q. Yan, Y. Li, and R. H. Deng, “Understanding OSNbased facial disclosure against face authentication systems,” Proc. 9th ACM Symp. Information, Comput. Commun. Secur. - ASIA CCS ’14, pp. 413–424, 2014.

A. Anjos and S. Marcel, “Counter-measures to photo attacks in face recognition: A public database and a baseline,” 2011 Int. Jt. Conf. Biometrics, IJCB 2011, 2011.

X. Tan, Y. Li, J. Liu, and L. Jiang, “Face Liveness Detection from A Single Image with Sparse Low Rank Bilinear Discriminative Model,” in Computer Vision – ECCV 2010, 2010.

G. Pan et al., “Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera,” 11th IEEE ICCV, Rio Janeiro, Brazil, Oct., vol. 14, p. 20, 2007.

I. Chingovska, A. Anjos, and E. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” Int. Conf. Biometrics Spec. Interes. Gr., pp. 1–7, 2012.

Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A Face Antispoofing Database with Diverse Attacks,” in IAPR International Conference on Biometrics (ICB), 2012, pp. 2–7.

K. Patel, H. Han, A. K. Jain, and G. Ott, “Live face video vs. spoof face video: Use of moiré patterns to detect replay video attacks,” Proc. 2015 Int. Conf. Biometrics, ICB 2015, pp. 98–105, 2015.

S. Bharadwaj, S. Member, T. I. Dhamecha, and S. Member, “Face Antispoofing via Motion Magnification and Multifeature Videolet Aggregation,” no. ii, pp. 1–12.

N. Kose and J. L. Dugelay, “Mask spoofing in face recognition and countermeasures,” Image Vis. Comput., vol. 32, no. 10, pp. 779–789, 2014.

“ThatsMyFace.” [Online]. Available: http://www.thatsmyface.com/.

M. N. H. Mohd, M. Kashima, K. Sato, and M. Watanabe, “Internal state measurement from facial stereo thermal and visible sensors through svm classification,” ARPN J. Eng. Appl. Sci., vol. 10, no. 18, pp. 8363– 8371, 2015.

M. Mohd, M.N.H., Kashima, M., Sato, K., Watanabe, “A non-invasive facial visual-infrared stereo vision based measurement as an alternative for physiological measurement,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, pp. 684–697.

M. De Marsico, M. Nappi, D. Riccio, and J. L. Dugelay, “Moving face spoofing detection via 3D projective invariants,” Proc. - 2012 5th IAPR Int. Conf. Biometrics, ICB 2012, pp. 73–78, 2012.

K. Kollreider, H. Fronthaler, M. I. Faraj, and J. Bigun, “Real-Time Face Detection and Motion Analysis With Application in ‘ Liveness ’ Assessment,” Analysis, vol. 2, no. 3, pp. 548–558, 2007.

G. Pan, Z. Wu, and L. Sun, “Liveness Detection for Face Recognition,” in Recent Advances in Face Recognition, 2008, pp. 109–124.

Z. Akhtar and G. L. Foresti, “Face Spoof Attack Recognition Using Discriminative Image Patches,” J. Electr. Comput. Eng., vol. 2016, 2016.

K. Kollreider, H. Fronthaler, and J. Bigun, “Non-intrusive liveness detection by face images,” Image Vis. Comput., vol. 27, no. 3, pp. 233– 244, 2009.

D. Wen, H. Han, and A. K. Jain, “Face Spoof Detection with Image Distortion Analysis,” IEEE Trans. Inf. Forensics Secur., vol. 10, no. 4, pp. 746–761, 2015.

D. C. Garcia and R. L. De Queiroz, “Face-Spoofing 2D-Detection Based on Moiré-Pattern Analysis,” IEEE Trans. Inf. Forensics Secur., vol. 10, no. 4, pp. 778–786, 2015.

D. C. . Garcia and R. L. De Queiroz, “MOIRE-PATTERN-BASED FACE-SPOOFING DETECTION,” Int. Conf. Image Process., vol. 1, pp. 4843–4847, 2015.

“The MSU Mobile Face Spoofing Database (MFSD).” [Online]. Available: https://www.cse.msu.edu/rgroups/biometrics/Publications/Databases/ MSUMobileFaceSpoofing/index.htm.

W. Arti, B. Swapnil, B. Ashwini, and V. Mohini, “Secure Face Spoof Detection on Smartphone,” Int. Res. J. Eng. Technol., vol. 3, no. 10, pp. 167–170, 2016.

“Unconstrained Smartphone Spoof Attack Database (USSA).” [Online]. Available: http://biometrics.cse.msu.edu/Publications/Databases/MSU_USSA/.

A. Costa-pazo, S. Bhattacharjee, E. Vazquez-fernandez, and S. Marcel, “The REPLAY-MOBILE Face Presentation-Attack Database,” in Proceedings of the International Conference on Biometrics Special Interests Group (BioSIG), 2016.

“Replay-Mobile Database.” [Online]. Available: https://www.idiap.ch/dataset/replay-mobile.

S. Bengio and J. Mariethoz, “A Statistical Significance Test For Person Authentication,” in ODYSSEY04 - The Speaker and Language Recognition Workshop, Toledo, Spain, 2004, no. 2.

M. E. Schuckers, Computational Methods in Biometric Authentication: Statistical Methods for Performance Evaluation. Springer-Verlag London, 2010.

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Published

2017-11-30

How to Cite

Abdul Ghaffar, I., & Haji Mohd, M. N. (2017). Presentation Attack Detection for Face Recognition on Smartphones: A Comprehensive Review. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-8), 33–38. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3095

Issue

Vol. 9 No. 3-8: Recent Trend in Electronic, Computing and Communication

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Articles

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