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Arabian Journal for Science and Engineering

, Volume 44, Issue 8, pp 7203–7217 | Cite as

An Improved Revocable Fuzzy Vault Scheme for Face Recognition Under Unconstrained Illumination Conditions

  • Chafia Ferhaoui Cherifi
  • Mohamed DericheEmail author
  • Khaled-Walid Hidouci
Research Article - Electrical Engineering
  • 16 Downloads

Abstract

The paper presents an improved fuzzy vault approach for face recognition under unconstrained environments. First, we parameterize the number of chaff points needed and determine the threshold separating the genuine points from the chaff points in the vault. The second improvement consists of enhancing the security of the fuzzy vault. Clancy et al. (in: Proceedings of ACM SIGMM2003, 2003) and Mihailescu (in: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR), 2007) discussed brute-force attacks to prove the weaknesses of the popular Juels and Sudan fuzzy vault method for some parameters such as the total number of points in the vault. To remedy such limitations, we introduce a cancellable and revocable biometric approach for face recognition based on the local binary patterns histograms. In addition to the revocability of the biometric data, we also achieved very recognition accuracy of more than 95% outperforming many of the exiting biometric approaches. More importantly, the proposed approach is developed to secure biometric data and achieve excellent recognition accuracy even under unconstrained environments.

Keywords

Biometrics Fuzzy vault Cryptography Revocability Unconstrained face images 

Notes

Acknowledgements

This work has been partially supported by KFUPM under DSR project no. SB151001.

References

  1. 1.
    Ross, A.: Information fusion in fingerprint authentication. Ph.D. Thesis, Michigan State University (2003)Google Scholar
  2. 2.
    Ratha, N.K.; Connell, J.H.; Bolle, R.M.: An analysis of minutiae matching strength. In: Proceedings AVBPA 2001, Third International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 223–228 (2001)Google Scholar
  3. 3.
    Oppliger, R.: Contemporary Cryptography. Computer Security Series. Artech House, Boston (2005)zbMATHGoogle Scholar
  4. 4.
    Riccio, D.; Galdi, C.; Manzo, R.: Biometric/cryptographic keys binding based on function minimization. In: 12th International Conference on Signal-Image Technology & Internet-Based Systems (2016).  https://doi.org/10.1109/sitis.2016.31
  5. 5.
    Catuogno, L.; Galdi, C.; Riccio, D.: Off-line enterprise rights management leveraging biometric key binding and secure hardware. J. Ambient Intell. and Humanized Computing, pp. 1–12 (2018)Google Scholar
  6. 6.
    Juels, A.; Sudan, M.: A fuzzy vault scheme. In: Proceedings of IEEE International Symposium on Information Theory, p. 408 (2002)Google Scholar
  7. 7.
    Ojala, T.; Pietikäinen, M.; Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  8. 8.
    Wang, Y.; Plataniotis, K.N.: Fuzzy vault for face based cryptographic key generation. In: Biometrics Symposium, 2007 E-ISBN: 978-1-4244-1549-6, Date of Conference: 11–13 Sept. 2007, pp. 1–6 (2007)Google Scholar
  9. 9.
    Goh, A.; Ngo, D.C.L.: Computation of cryptographic keys from face biometrics. In: International Federation for Information Processing, pp. 1–13 (2003)Google Scholar
  10. 10.
    Linnartz, J.P.; Tuyls, P.: New shielding functions to enhance privacy and prevent misuse of biometric templates In: Proceedings 4th International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 393–402 (2003)Google Scholar
  11. 11.
    Davida, G.I.; Frankel, Y.; Matt, B.J.: On enabling secure applications through on-line biometric identification. In: Proceeding of IEEE Symposium on Privacy and Security, pp. 148–157 (1998)Google Scholar
  12. 12.
    Juels, A.; Wattenberg, M.: A fuzzy commitment scheme. In: IEEE International Symposium on Information Theory, pp. 408–413 (2002)Google Scholar
  13. 13.
    Clancy, T.C.; Kiyavash, N.; Lin, D.J.: Secure smartcard based fingerprint authentication. In: Proceedings of ACM SIGMM2003, pp. 45–52 (2003)Google Scholar
  14. 14.
    Yang, S.; Verbauwhede, I.: Automatic secure fingerprint verification system based on fuzzy vault scheme. In: Proceedings of International Conference on Acoustic, Speech and Signal Processing (2005)Google Scholar
  15. 15.
    Uludag, U.; Jain, A.K.: Securing fingerprint template: fuzzy vault with helper data. In: Proceedings IEEE Workshop on Privacy Research in Vision, p. 163, June 22 (2006)Google Scholar
  16. 16.
    Nandakumar, K.; Jain, A.K.; Pankanti, S.: Fingerprint-based fuzzy vault: implementation. IEEE Trans. Inf. Forensico Secur. 2(4), 744–757 (2007)CrossRefGoogle Scholar
  17. 17.
    Nagar, A.; Nandakumar, K.; Jain, A.K.: Securing fingerprint template fuzzy vault with minutiae descriptors. 19th Int. Conf. Patt. Rec., pp. 1–4 (2008)Google Scholar
  18. 18.
    Nagar, A.; Nandakumar, K.; Jain, A.K.: Technical Report: Multibiometric Cryptosystems. IEEE TIFTS (under review)Google Scholar
  19. 19.
    Meenakshi, V.S.; Padmavathi, G.: Securing revocable iris and retinal templates using combined user and soft biometric based password hardened multimodal fuzzy vault. IJCSI Int. J. Comput. Sci. Issues 7(5), 159–167 (2010)Google Scholar
  20. 20.
    Linh Vo, T.T.; Dang, T.K.; Küng, J.: A Hash-Based Index Method for Securing Biometric Fuzzy Vaults, pp. 60–71. LNCS 8647Springer International Publishing, Bern (2014)Google Scholar
  21. 21.
    Bao Le, T.T.; Dang, T.K.; Truong, Q.C.; Nguyen, T.A.T.: Protecting Biometric Features by Periodic Function-Based Transformation and Fuzzy Vault, pp. 57–70. LNCS 8960Springer, Berlin (2014)Google Scholar
  22. 22.
    Tams, B.: Unlinkable minutiae-based fuzzy vault for multiple fingerprints. IET Biometr. 5(3), 170–180 (2015)CrossRefGoogle Scholar
  23. 23.
    Nguyen, T.H.; Wang, Y.; Ha, Y.; Li, R.: Performance and security-enhanced fuzzy vault scheme based on ridge features for distorted fingerprints. IET Biometr. 4(1), 29–39 (2015)CrossRefGoogle Scholar
  24. 24.
    Kasaei, S.; Deriche, M.; Boashash, B.: Fingerprint feature extraction using block-direction on reconstructed images. In: Proceedings of IEEE TENCON ‘97. IEEE Region 10 Annual Conference. vol. 1, pp. 303–306, Brisbane, Australia (1997)Google Scholar
  25. 25.
    Khandelwal, S.; Gupta, P.C.: Protecting biometric features by periodic function-based transformation and fuzzy vault. In: Satapathy, S.C., et al. (eds.) Emerging ICT for Bridging the Future, vol. 1311. Springer International Publishing, Bern (2015)Google Scholar
  26. 26.
    Hadid, A.; Ahonen, T.; Pietikäinen, M.: Computer Vision Using Local Binary Patterns, vol. 40. Springer, Berlin (2011)zbMATHGoogle Scholar
  27. 27.
    Ojala, T.; Pietikäinen, M.; Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  28. 28.
    Wua, L.; Yuana, S.: A face based fuzzy vault scheme for secure online authentication. In: Second International Symposium on Data, Privacy, and E-Commerce (2010)Google Scholar
  29. 29.
    Laboratories Cambridge, ORL face database. www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
  30. 30.
    Ahonen, T.; Pietikäinen, M.: Pixelwise local binary pattern models of faces using kernel density estimation. In ICB, pp. 52–61 (2009)Google Scholar
  31. 31.
    Rodriguez, Y.: Face detection and verification using local binary patterns. Ph.D. Thesis at, la faculté des sciences et techniques de l’ingénieur, Ecole Polytechnique Fédérale de Lausanne, Suisse (2006)Google Scholar
  32. 32.
    Boutellaa, E.; Bengherabi, M.; Boulkenafet, Z.; Harizi, F.; Hadid, A.: Face verification using local binary patterns generic histogram adaptation and Chi square based decision. In: 4th European Workshop on Visual Information Processing (EUVIP), pp. 142–147 (2013)Google Scholar
  33. 33.
    Lu, H.; Martin, K.; Bui, F.; Plataniotis, K.N.; Hatzinakos, D.: Face recognition with biometric encryption for privacy-enhancing self-exclusion. In: Digital Signal Processing (2009)Google Scholar
  34. 34.
    Martin, K.; Lu, H.; Minhthang Bui, F.; Plataniotis, K.N.; Hatzinakos, D.: A biometric encryption system for the self-exclusion scenario of face recognition. Syst. J. IEEE 3(4), 440–450 (2009)CrossRefGoogle Scholar
  35. 35.
    Sapkal, S.; Shrishrimal, P.; Deshmukh, R.R.: Face verification using scale invariant feature transform with template security. In: Fuzzy Systems (FUZZ-IEEE) (2017)Google Scholar
  36. 36.
    Krizaj, J., Struc, V.; Pavesic, N.: Adaptation of SIFT features for robust face recognition. In: ICIAR 2010, Part I, LNCS 6111, pp. 394–404 (2010)Google Scholar
  37. 37.
    Geng, C.; Jiang, X.: Face recognition using SIFT features. In: ICIP’09 Proceedings of the 16th IEEE International Conference on Image Processing, pp. 3277–3280 (2009)Google Scholar
  38. 38.
    Lenc, L.; Král, P.: Automatic face recognition system based on the SIFT features. Comput. Electr. Eng. 46, 256–272 (2015)CrossRefGoogle Scholar
  39. 39.
    Ameen, M.M.; Eleyan, A.: Score fusion of SIFT & SURF descriptors for face recognition using wavelet transforms. J. Image Graph. Signal Process. 10, 22–28 (2017).  https://doi.org/10.5815/ijigsp.2017.10.03 CrossRefGoogle Scholar
  40. 40.
    Shruti Biswal, M.: Feature extraction of face using various techniques. B.Tech. thesis, National Institute of Technology Rourkela, Rourkela, IndiaGoogle Scholar
  41. 41.
    Nithya, B.; Bhavani Sankari, Y.; Manikantan, K.; Ramachandran, S.: Discrete orthonormal Stockwell transform based feature extraction for pose invariant face recognition. In: International Conference on Advanced Computing Technologies and Applications (ICACTA-2015) (2015)Google Scholar
  42. 42.
    Anand, B.; Shah, P.K.: Face recognition using SURF features and SVM classifier. Int. J. Electron. Eng. Res. 8(1), 1–8 (2016)Google Scholar
  43. 43.
    Du, G.; Su, F.; Cai, A.: Face recognition using SURF features. In: MIPPR 2009: Pattern Recognition and Computer Vision, Proceedings of SPIE vol. 7496, 749628.  https://doi.org/10.1117/12.832636
  44. 44.
    Vinay, A.; Vasuki, V.; Bhat, S.; Jayanth, K.S.; Balasubramanya Murthy, K.N.; Natarajan, S.: Two dimensionality reduction techniques for SURF based face recognition. In: International Conference on Computational Modeling and Security (CMS 2016) (2016)Google Scholar
  45. 45.
    Ahonen, T.; Hadid, A.; Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
  46. 46.
    Werghi, N.; Tortorici, C.; Berretti, S.; Del Bimbo, A.: Boosting 3D LBP-based face recognition by fusing shape and texture descriptors on the mesh. IEEE Trans. Inf. Forensics Secur. 11(5), 964–979 (2016)CrossRefGoogle Scholar
  47. 47.
    Mihailescu, P.: The fuzzy vault for fingerprints is vulnerable to brute force attack. In: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR), 22 Aug 2007 (2007)Google Scholar
  48. 48.
    Khalil-hani, M.; Marsono, M.N.; Bakhteri, R.: Biometric encryption based on a fuzzy vault scheme with a fast chaff generation algorithm. Fut. Gener. Comput. Syst. 29(3), 800–810 (2013)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Laboratoire de la Communication dans les Systèmes InformatiquesEcole Nationale Supérieure d’InformatiqueOued-Smar, AlgerAlgeria
  2. 2.King Fahd University of Petroleum & MineralsDhahranSaudi Arabia

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