Skip to main content
SpringerLink
Log in

Random permutation principal component analysis for cancelable biometric recognition

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Although biometrics is being increasingly used across the world, it also raises concerns over privacy and security of the enrolled identities. This is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. To address these problems, in this paper, we suggest two simple and powerful techniques called (i) Random Permutation Principal Component Analysis (RP-PCA) and (ii) Random Permutation Two Dimensional Principal Component Analysis (RP-2DPCA). The proposed techniques are based on the idea of cancelable biometric which can be reissued if compromised. The proposed techniques work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The identity of a person is recognized only if the combination of template and PIN is valid, otherwise the identity is rejected. The superiority of the proposed techniques is demonstrated on three freely available face (ORL), iris (UBIRIS) and ear (IITD) datasets against state-of-the-art methods. The key advantages of the proposed techniques are (i) classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation (ii) robustness across different biometrics. In addition, no image registration is required for performing recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Prabhakar S, Pankanti S, Jain AK (2003) Biometric recognition: Security and privacy concerns. IEEE Secur Privacy Mag 1(2):33–42

    Article  Google Scholar 

  2. Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20

    Article  Google Scholar 

  3. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

  4. Douxchamps D, Campbell N (2007) Robust real time face tracking for the analysis of human behaviour. In: Proceedings of the 4th International Conference on Machine Learning for Multimodal Interaction, Berlin, Heidelberg, pp 1–10

  5. Peralta D, Galar M, Triguero I, Paternain D, García S, Barrenechea E, Benítez JM, Bustince H, Herrera F (2015) A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation. Inf Sci 315(10):67–87

    Article  MathSciNet  Google Scholar 

  6. Marsico MD, Petrosino A, Ricciardi S (2016) Iris recognition through machine learning techniques: a survey, Pattern Recognition Letters, Available online

  7. Gong Y (1995) Speech recognition in noisy environments: a survey. Speech Comm 16(3):261–291

    Article  MathSciNet  Google Scholar 

  8. Bellman RE (1961) Adaptive control processes. Princeton University Press, Princeton

    Book  MATH  Google Scholar 

  9. Cunningham JP, Ghahramani Z (2015) Linear dimensionality reduction: survey, insights, and generalizations. J Mach Learn Res 16:2859–2900

    MathSciNet  MATH  Google Scholar 

  10. Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2:559–572

    Article  MATH  Google Scholar 

  11. Hotelling H (1936) Relations between two sets of variates. Biometrika 28:321–377

    Article  MATH  Google Scholar 

  12. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7(2):179–188

    Article  Google Scholar 

  13. Cox TF, Cox MA (2001) Multidimensional scaling. CRC Press, Boca Raton

    MATH  Google Scholar 

  14. Wiskott L, Sejnowski T (2002) Slow feature analysis: unsupervised learning of invariances. Neural Comput 14(4):715–770

    Article  MATH  Google Scholar 

  15. Wiskott L (2003) Slow feature analysis: a theoretical analysis of optimal free responses. Neural Comput 15 (9):2147–2177

    Article  MATH  Google Scholar 

  16. He X, Niyogi P (2004) Locality preserving projections. In: Advances in Neural Information Processing Systems, p 16

  17. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  18. Sayed A, Sardeshmukh M, Limkar S (2014) Improved iris recognition using Eigen values for feature extraction for off gaze images, ICT and critical infrastructure. In: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II: Hosted by CSI Vishakapatnam Chapter, pp 181–189

  19. Yongxu W, Xinyu A, Yuanfeng D, Li Yongping (2006) A fingerprint recognition algorithm based on principal component analysis, TENCON 2006 - 2006 IEEE region 10 conference, Hong Kong

  20. Jing X, Ma J, Zhao J, Yang H (2014) Speaker recognition based on principal component analysis of LPCC and MFCC, signal Processing. In: 2014 IEEE International Conference on Communications and Computing (ICSPCC), Guilin, pp 403–408

  21. Yang J, Zhang D, Frangi AF, Yang J (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137

    Article  Google Scholar 

  22. Patel VM, Ratha NK, Chellappa R (2015) Cancelable Biometrics: a review. IEEE Signal Process Mag 32(5):54–65

    Article  Google Scholar 

  23. Ratha NK, Connel JH, Bolle R (2001) Enhancing security and privacy in biometrics- based authentication systems. IBM Syst J 40(3):614–634

    Article  Google Scholar 

  24. Ratha N, Chikkerur S, Connell J, Bolle R (2007) Generating cancelable fingerprint templates. IEEE Trans Pattern Anal Mach Intell 29(4):561–572

    Article  Google Scholar 

  25. Bolle RM, Connel JH, Ratha NK (2002) Biometrics perils and patches. Pattern Recogn 35(12):2727–2738

    Article  MATH  Google Scholar 

  26. Pillai JK, Patel VM, Chellappa R, Ratha NK (2011) Secure and robust iris recognition using random projections and sparse representations. IEEE Trans Pattern Anal Mach Intell 30(9):1877–1893

    Article  Google Scholar 

  27. Savvides M, Kumar B, Khosla P (2004) Cancelable biometric filters for face recognition. Proc Int Conf Pattern Recognition 3:922–925

    Google Scholar 

  28. Maiorana E, Campisi P, Fierrez J, Ortega-Garcia J, Neri A (2010) Cancelable templates for sequence-based biometrics with application to on-line signature recognition. IEEE Trans Syst., Man Cybern A 40 (3):525–538

    Article  Google Scholar 

  29. Rathgeb C, Breitinger F, Busch C, Baier H (2014) On the application of bloom filters to iris biometrics. IET J Biometrics 3(4):207–218

    Article  Google Scholar 

  30. Xu W, He Q, Li Y, Li T (2008) Cancelable voiceprint templates based on knowledge signatures. In: Proceedings of International Symposium Electronic Commerce and Security, pp 412–415

  31. Teoh A, Goh A, Ngo D (2006) 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

    Article  Google Scholar 

  32. Zuo J, Ratha N, Connell J (2008) Cancelable iris biometric. In: Proceedings of the International Conference on Pattern Recognition, pp 1–4

  33. Hammerle-Uhl J, Pschernig E, Uhl A, Samarati P, Yung M, Martinelli F, Ardagna CA (2009) Cancelable iris biometrics using block re-mapping and image warping. In: Proceedings of the 12th International Conference on Information Security, Pisa, Italy, pp 135–142

  34. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  35. Kumar N, Jaiswal A, Agrawal RK (2012) Performance evaluation of subspace methods to tackle small sample size problem in face recognition. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI ’12). ACM, New York, pp 938–944

  36. Tan X, Chen S, Zhou ZH, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39(9):1725–1745

    Article  MATH  Google Scholar 

  37. Samaria F, Harter A (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL

  38. Proença H, Alexandre LA (2005) UBIRIS: a noisy iris image database. In: 13th International Conference on Image Analysis and Processing - ICIAP 2005, Springer, vol. LNCS 3617, pp 970–977

  39. Kumar A, Wu C (2012) Automated human identification using ear imaging. Pattern Recogn 41(5):956–968

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Uttarakhand, Srinagar Garhwal, 246174, India

    Nitin Kumar, Surendra Singh & Abhimanyu Kumar

Authors
  1. Nitin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhimanyu Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nitin Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, N., Singh, S. & Kumar, A. Random permutation principal component analysis for cancelable biometric recognition. Appl Intell 48, 2824–2836 (2018). https://doi.org/10.1007/s10489-017-1117-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-017-1117-7

Keywords

Search

Navigation