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Deep Learning for Biometrics pp 259–285Cite as

Learning Representations for Cryptographic Hash Based Face Template Protection

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

In this chapter, we discuss the impact of recent advancements in deep learning in the field of biometric template protection. The representation learning ability of neural networks has enabled them to achieve state-of-the-art results in several fields, including face recognition. Consequently, biometric authentication using facial images has also benefited from this, with deep convolutional neural networks pushing the matching performance numbers to all time highs. This chapter studies the ability of neural networks to learn representations which could benefit template security in addition to matching accuracy. Cryptographic hashing is generally considered most secure form of protection for the biometric data, but comes at the high cost of requiring an exact match between the enrollment and verification templates. This requirement generally leads to a severe loss in matching performance (FAR and FRR) of the system. We focus on two relatively recent face template protection algorithms that study the suitability of representations learned by neural networks for cryptographic hash based template protection. Local region hashing tackles hash-based template security by attempting exact matches between features extracted from local regions of the face as opposed to the entire face. A comparison of the suitability of different feature extractors for the task is presented and it is found that a representation learned by an autoencoder is the most promising. Deep secure encoding tackles the problem in an alternative way by learning a robust mapping of face classes to secure codes which are then hashed and stored as the secure template. This approach overcomes several pitfalls of local region hashing and other face template algorithms. It also achieves state-of-the-art matching performance with a high standard of template security.

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References

  1. M. Ao, S.Z. Li, Near infrared face based biometric key binding, in Advances in Biometrics (Springer, Berlin, 2009), pp. 376–385

    Chapter  Google Scholar 

  2. F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I.J. Goodfellow, A. Bergeron, N. Bouchard, Y. Bengio, Theano: new features and speed improvements, in Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop (2012)

    Google Scholar 

  3. J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, Y. Bengio, Theano: a CPU and GPU math expression compiler, in Proceedings of the Python for Scientific Computing Conference (SciPy) (2010) (Oral Presentation)

    Google Scholar 

  4. B. Chen, V. Chandran, Biometric based cryptographic key generation from faces, in 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (IEEE, 2007), pp. 394–401

    Google Scholar 

  5. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1 (IEEE, 2005), pp. 886–893

    Google Scholar 

  6. Y. Dodis, L. Reyzin, A. Smith, Fuzzy extractors: how to generate strong keys from biometrics and other noisy data, in International Conference on the Theory and Applications of Cryptographic Techniques (Springer, 2004), pp. 523–540

    Chapter  Google Scholar 

  7. Y.C. Feng, P.C. Yuen, Binary discriminant analysis for generating binary face template. IEEE Trans. Inf. Forensics Secur. 7(2), 613–624 (2012)

    Article  Google Scholar 

  8. Y.C. Feng, P.C. Yuen, A.K. Jain, A hybrid approach for generating secure and discriminating face template. IEEE Trans. Inf. Forensics Secur. 5(1), 103–117 (2010)

    Article  Google Scholar 

  9. A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  10. R. Gross, I. Matthews, J. Cohn, T. Kanade, S. Baker, Multi-pie. Image Vis. Comput. 28(5), 807–813 (2010)

    Article  Google Scholar 

  11. G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv:1207.0580

  12. A.K. Jain, K. Nandakumar, A. Nagar, Biometric template security. EURASIP J. Adv. Signal Process. 2008, 113 (2008)

    Google Scholar 

  13. Y. Kim, K.-A. Toh, A method to enhance face biometric security, in First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007 (IEEE, 2007), pp. 1–6

    Google Scholar 

  14. A. Kraskov, H. Stögbauer, P. Grassberger, Estimating mutual information. Phys. Rev. E 69(6), 066138 (2004)

    Article  MathSciNet  Google Scholar 

  15. Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, in Proceedings of the IEEE (1998), pp. 2278–2324

    Article  Google Scholar 

  16. H. Lu, K. Martin, F. Bui, K.N. Plataniotis, D. Hatzinakos, Face recognition with biometric encryption for privacy-enhancing self-exclusion, in 2009 16th International Conference on Digital Signal Processing (IEEE, 2009), pp. 1–8

    Google Scholar 

  17. D.C.L. Ngo, A.B.J. Teoh, A. Goh, Biometric hash: high-confidence face recognition. IEEE Trans. Circuits Syst. Video Technol. 16(6), 771–775 (2006)

    Article  Google Scholar 

  18. T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  19. R.K. Pandey, V. Govindaraju, Secure face template generation via local region hashing, in 2015 International Conference on Biometrics (ICB) (IEEE, 2015), pp. 1–6

    Google Scholar 

  20. M. Savvides, B.V.K. Vijaya Kumar, P.K. Khosla, Cancelable biometric filters for face recognition, in ICPR 2004, vol. 3 (IEEE, 2004), pp. 922–925

    Google Scholar 

  21. F. Schroff, D. Kalenichenko, J. Philbin, Facenet: a unified embedding for face recognition and clustering, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 815–823

    Google Scholar 

  22. T. Sim, S. Baker, M. Bsat, The CMU pose, illumination, and expression (PIE) databas, in Proceedings. Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002 (IEEE, 2002), pp. 46–51

    Google Scholar 

  23. Y. Sutcu, H.T. Sencar, N. Memon, A secure biometric authentication scheme based on robust hashing, in Proceedings of the 7th Workshop on Multimedia and Security (ACM, 2005), pp. 111–116

    Google Scholar 

  24. Y. Sutcu, Q. Li, N. Memon, Protecting biometric templates with sketch: theory and practice. IEEE Trans. Inf. Forensics Secur. 2(3), 503–512 (2007)

    Article  Google Scholar 

  25. Y. Taigman, M. Yang, M. Ranzato, L. Wolf, Deepface: closing the gap to human-level performance in face verification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2014), pp. 1701–1708

    Google Scholar 

  26. X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

  27. A. Teoh, C.T. Yuang, Cancelable biometrics realization with multispace random projections. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37(5), 1096–1106 (2007)

    Article  Google Scholar 

  28. A.B.J. Teoh, D.C.L. Ngo, A. Goh, Personalised cryptographic key generation based on facehashing. Comput. Secur. 23(7), 606–614 (2004)

    Article  Google Scholar 

  29. A.B.J. Teoh, A. Goh, D.C.L. Ngo, Random multispace quantization as an analytic mechanism for biohashing of biometric and random identity inputs. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1892–1901 (2006)

    Article  Google Scholar 

  30. M. Van Der Veen, T. Kevenaar, G.-J. Schrijen, T.H. Akkermans, F. Zuo, et al., Face biometrics with renewable templates, in Proceedings of SPIE, vol. 6072 (2006), pp. 60720J

    Google Scholar 

  31. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  32. Y. Wu, B. Qiu, Transforming a pattern identifier into biometric key generators, in ICME 2010 (IEEE, 2010), pp. 78–82

    Google Scholar 

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Authors and Affiliations

  1. Univeristy of Buffalo, SUNY, Buffalo, NY, 14260, USA

    Rohit Kumar Pandey, Yingbo Zhou, Bhargava Urala Kota & Venu Govindaraju

Authors
  1. Rohit Kumar Pandey
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  2. Yingbo Zhou
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  3. Bhargava Urala Kota
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  4. Venu Govindaraju
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Corresponding author

Correspondence to Rohit Kumar Pandey .

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Editors and Affiliations

  1. University of California, Riverside, California, USA

    Bir Bhanu

  2. Hong Kong Polytechnic University, Hong Kong, China

    Ajay Kumar

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Pandey, R.K., Zhou, Y., Kota, B.U., Govindaraju, V. (2017). Learning Representations for Cryptographic Hash Based Face Template Protection. In: Bhanu, B., Kumar, A. (eds) Deep Learning for Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-61657-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-61657-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61656-8

  • Online ISBN: 978-3-319-61657-5

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