RP-LPP : a random permutation based locality preserving projection for cancelable biometric recognition

  • Nitin KumarEmail author
  • Manisha Rawat


Biometrics are being increasingly used across the world, but it also raises privacy and security concerns of the enrolled identities. The main reason is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. Cancelable biometric template is a solution to this problem which can be reissued if compromised. In this paper, we suggest a simple and powerful method called Random Permutation Locality Preserving Projection (RP-LPP) for Cancelable Biometric Recognition. Here, we exploit the mathematical relationship between the eigenvalues and eigenvectors of the original biometric image and its randomly permuted version is exploited for carrying out cancelable biometric recognition. The proposed technique work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The effectiveness 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 advantages of proposed technique are (i) the classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation, (ii) security and quality of generated templates and (iii) robustness across different biometrics. In addition, no image registration is required for performing recognition.


Cryptic Revocable PIN Single sample 



  1. 1.
    Al-juboori AM, Bu W, Wu X, Zhao Q (2014) Palm vein verification using multiple features and locality preserving projections. The Scientific World Journal, 2014, Article ID 246083, 11 pagesGoogle Scholar
  2. 2.
    Bellman RE (1961) Adaptive control processes. Princeton University Press, PrincetonCrossRefGoogle Scholar
  3. 3.
    Bolle RM, Connel JH, Ratha NK (2002) Biometrics perils and patches. Pattern Recogn 35(12):2727–2738CrossRefGoogle Scholar
  4. 4.
    Cox TF, Cox MA (2001) Multidimensional scaling. CRC Press, Boca RatonzbMATHGoogle Scholar
  5. 5.
    Cunningham JP, Ghahramani Z (2015) Linear dimensionality reduction: survey, insights, and generalizations. J Mach Learn Res 16:2859–2900MathSciNetzbMATHGoogle Scholar
  6. 6.
    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 1–10Google Scholar
  7. 7.
    Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2):179–188CrossRefGoogle Scholar
  8. 8.
    Gao Q, Zhang C (2017) Constructing cancellable template with synthetic minutiae. IET Biometrics 6(6):448–456CrossRefGoogle Scholar
  9. 9.
    Gong Y (1995) Speech recognition in noisy environments: a survey. Speech Comm 16(3):261–291MathSciNetCrossRefGoogle Scholar
  10. 10.
    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: Proc. of the 12th international conference on information security, Pisa, Italy, pp 135–142Google Scholar
  11. 11.
    He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems, p 16Google Scholar
  12. 12.
    Hotelling H (1936) Relations between two sets of variates. Biometrika 28:321–377CrossRefGoogle Scholar
  13. 13.
    Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circ Syst Video Technol 14(1):4–20CrossRefGoogle Scholar
  14. 14.
    Jin Z, Hwang JY, Lai Y, Kim S, Teoh ABJ (2018) Ranking-based locality sensitive hashing-enabled cancelable biometrics: index-of-max hashing. IEEE Trans Inform Forensics Secur 13(2):393–407CrossRefGoogle Scholar
  15. 15.
    Kaur H, Khanna P (2017) Non-invertible biometric encryption to generate cancelable biometric templates. In: Proc. of the world congress on engineering and computer science (WCECS 2017), USAGoogle Scholar
  16. 16.
    Kaur H, Khanna P (2019) Random distance method for generating unimodal and multimodal cancelable biometric features. IEEE Trans Inform Forensics Secur 14 (3):709–719CrossRefGoogle Scholar
  17. 17.
    Kumar A, Wu C (2012) Automated human identification using ear imaging. Pattern Recogn 41(5):956–968CrossRefGoogle Scholar
  18. 18.
    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, NY, USA, pp 938–944Google Scholar
  19. 19.
    Lee DH, Lee S, Ik Cho N (2018) Cancelable biometrics using noise embedding. In: Proc. of the 24th international conference on pattern recognition (ICPR), pp 3390–3395Google Scholar
  20. 20.
    Leng L, Li M, Leng L, Teoh ABJ (2013) Conjugate 2DPalmhash code for secure palm-print-vein verification. In: Proc. of the 6th international congress on image and signal processing (CISP), Hangzhou, pp 1705–1710Google Scholar
  21. 21.
    Leng L, Teoh ABJ (2015) Alignment-free row-co-occurrence cancelable palmprint. Fuzzy Vault Pattern Recogn 48(7):2290–2303. CrossRefGoogle Scholar
  22. 22.
    Leng L, Teoh AB, Li M (2017) Simplified 2DPalmHash code for secure palmprint verification. Multimed Tools Appl 76(6):8373–8398. CrossRefGoogle Scholar
  23. 23.
    Leng L, Zhang J (2013) PalmHash Code vs. PalmPhasor Code. Neurocomputing 108:1–12. CrossRefGoogle Scholar
  24. 24.
    Leng L, Zhang J, Xu J, Khan K, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. Int J Phys Sci 5:467–471Google Scholar
  25. 25.
    Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In: Proc. of international conference on information and communication technology convergence (ICTC), Jeju, 2010, pp 467–471Google Scholar
  26. 26.
    Leng L, Zhang J, Chen G, Khan MK, Alghathbar K (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. In: Murgante B, Gervasi O, Iglesias A, Taniar D, Apduhan BO (eds) Computational science and its applications - ICCSA 2011. Springer, Berlin, pp 458–470CrossRefGoogle Scholar
  27. 27.
    Leng L, Zhang J, Chen G, Khan MK, Bai P (2011) Two dimensional palmphasor enhanced by multi-orientation score level fusion. In: Park JJ, Lopez J, Yeo SS, Shon T, Taniar D (eds) Secure and trust computing, data management and applications. Springer, Berlin, pp 122–129Google Scholar
  28. 28.
    Leng L, Zhang JS (2012) Palmhash code for palmprint verification and protection. Proc. of 25th IEEE Canadian Conf. Electr. Comp. Eng.: 1–4Google Scholar
  29. 29.
    Leng L, Zhang S, Bi X, Khan MK (2012) Two-dimensional cancelable biometric scheme. In: Proc. of the international conference on wavelet analysis and pattern recognition, Xian, pp 164–169Google Scholar
  30. 30.
    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):525–538CrossRefGoogle Scholar
  31. 31.
    Marsico MD, Petrosino A, Ricciardi S (2016) Iris recognition through machine learning techniques: a survey. Pattern Recognition Letters, Available onlineGoogle Scholar
  32. 32.
    Patel VM, Ratha NK, Chellappa R (2015) Cancelable Biometrics: a review. IEEE Signal Processing Magazine 32(5):54–65CrossRefGoogle Scholar
  33. 33.
    Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2:559–572CrossRefGoogle Scholar
  34. 34.
    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. Inform Sci 315(10):67–87MathSciNetCrossRefGoogle Scholar
  35. 35.
    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):1877–1893CrossRefGoogle Scholar
  36. 36.
    Prabhakar S, Pankanti S, Jain AK (2003) Biometric recognition: security and privacy concerns. IEEE Security Privacy Mag 1(2):33–42CrossRefGoogle Scholar
  37. 37.
    Proença H, Alexandre LA (2005) UBIRIS: a noisy iris image database, 13th International Conference on Image Analysis and Processing - ICIAP 2005, Springer, vol. LNCS 3617, 970–977Google Scholar
  38. 38.
    Ratha N, Chikkerur S, Connell J, Bolle R (2007) Generating cancelable fingerprint templates. IEEE Trans Pattern Anal Mach Intell 29(4):561–572CrossRefGoogle Scholar
  39. 39.
    Ratha NK, Connel JH, Bolle R (2001) Enhancing security and privacy in biometrics- based authentication systems. IBM Syst J 40(3):614–634CrossRefGoogle Scholar
  40. 40.
    Rathgeb C, Breitinger F, Busch C, Baier H (2014) On the application of bloom filters to iris biometrics. IET J Biometrics 3(4):207–218CrossRefGoogle Scholar
  41. 41.
    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 FLGoogle Scholar
  42. 42.
    Savvides M, Kumar B, Khosla P (2004) Cancelable biometric filters for face recognition. Proc Int Conf Pattern Recognition 3:922–925CrossRefGoogle Scholar
  43. 43.
    Tan X, Chen S, Zhou ZH, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39(9):1725–1745CrossRefGoogle Scholar
  44. 44.
    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):1892–1901CrossRefGoogle Scholar
  45. 45.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognitive Neuroscience 3(1):71–86CrossRefGoogle Scholar
  46. 46.
    Wiskott L (2003) Slow feature analysis: a theoretical analysis of optimal free responses. Neural Comput 15(9):2147–2177CrossRefGoogle Scholar
  47. 47.
    Wiskott L, Sejnowski T (2002) Slow feature analysis: unsupervised learning of invariances. Neural Comput 14(4):715–770CrossRefGoogle Scholar
  48. 48.
    Wu S, Chen P, Swindlehurst AL, Hung P (2019) Cancelable biometric recognition with ECGs: subspace-based approaches. IEEE Trans Inform Forensics Secur 14(5):1323–1336CrossRefGoogle Scholar
  49. 49.
    Xu W, He Q, Li Y, Li T (2008) Cancelable voiceprint templates based on knowledge signatures. Proc Int Symp Electronic Commerce and Security: 412–415Google Scholar
  50. 50.
    Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458CrossRefGoogle Scholar
  51. 51.
    Zuo J, Ratha N, Connell J (2008) Cancelable iris biometric. Proc Int Conf Pattern Recognition: 1–4Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology, UttarakhandSrinagar GarhwalIndia

Personalised recommendations