Effectiveness of symmetric rejection for a secure and user convenient multistage biometric system

Abstract

A multistage biometric verification system uses multiple biometrics and/or multiple biometric verifiers to generate a verification decision. The core of a multistage biometric verification system is reject option which allows a stage not to give a genuine/impostor decision when it is not confident enough. This paper studies the effectiveness of symmetric rejection for multistage biometric verification systems. The symmetric rejection method determines the reject region by symmetrically rejecting equal proportion of genuine and impostor scores. The applicability of a multistage biometric verification system depends on how secure and user convenient it is, which is measured by the performance–cost trade-off. This paper analyzes the performance–cost trade-off of symmetric rejection method by conducting extensive experiments. Experiments are performed on two biometric databases: (1) publicly available NIST database and (2) a keystroke database. In addition, the symmetric rejection method is empirically compared with two existing rejection methods: (1) sequential probability ratio test-based method, which uses score-fusion and (2) Marcialis et al.’s method, which does not use score fusion. Results demonstrate strong effect of symmetric rejection method on creating a secure and user convenient multistage biometric verification system.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Abo-Zahhad M, Ahmed SM, Abbas SN (2016) A new multi-level approach to eeg based human authentication using eye blinking. Pattern Recognit Lett 82:216–225

    Article  Google Scholar 

  2. 2.

    Akhtar Z, Fumera G, Marcialis G, Roli F (2012) Evaluation of serial and parallel multibiometric systems under spoofing attacks. In: IEEE conference on biometrics: theory, applications and systems (BTAS), pp 283–288

  3. 3.

    Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S, Nagem TAM (2018) A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal Appl 21(3):783–802

    MathSciNet  Article  Google Scholar 

  4. 4.

    Allano L, Dorizzi B, Garcia-Salicetti S (2010) Tuning cost and performance in multi-biometric systems: a novel and consistent view of fusion strategies based on the sequential probability ratio test (sprt). Pattern Recognit Lett 31(9):884–890

    Article  Google Scholar 

  5. 5.

    Baig A, Bouridane A, Kurugollu F, Albesher B (2014) Cascaded multimodal biometric recognition framework. IET Biometr 3(1):16–28

    Article  Google Scholar 

  6. 6.

    Bartlett PL, Wegkamp MH (2008) Classification with a reject option using a hinge loss. J Mach Learn Res 9:1823–1840

    MathSciNet  MATH  Google Scholar 

  7. 7.

    Bharadwaj S, Bhatt HS, Singh R, Vatsa M, Noore A (2015) Qfuse: Online learning framework for adaptive biometric system. Pattern Recognit 48(11):3428–3439

    Article  Google Scholar 

  8. 8.

    Bhatt H, Bharadwaj S, Vatsa M, Singh R, Ross A, Noore A (2011) A framework for quality-based biometric classifier selection. In: 2011 international joint conference on biometrics (IJCB), pp 1–7

  9. 9.

    Chow CK (1970) On optimum recognition error and reject tradeoff. IEEE Trans Inf Theory 16(1):41–46

    Article  Google Scholar 

  10. 10.

    Das RK, Jelil S, Mahadeva Prasanna SR (2017) Development of multi-level speech based person authentication system. J Signal Process Syst 88(3):259–271

    Article  Google Scholar 

  11. 11.

    Dinca LM, Hancke GP (2017) The fall of one, the rise of many: a survey on multi-biometric fusion methods. IEEE Access 5:6247–6289

    Article  Google Scholar 

  12. 12.

    Dwivedi R, Dey S (2018) A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification. Appl Intell 49(3):1016–1035

    Article  Google Scholar 

  13. 13.

    Elhoseny M, Essa E, Elkhateb A, Hassanien A, Hamad A (2018) Cascade multimodal biometric system using fingerprint and iris patterns. In: International conference on advanced intelligent systems and informatics, pp 590–599

  14. 14.

    Fierrez J, Morales A, Vera-Rodriguez R, Camacho D (2018) Multiple classifiers in biometrics. Part 1: fundamentals and review. Inf Fusion 44:57–64

    Article  Google Scholar 

  15. 15.

    Fierrez J, Morales A, Vera-Rodriguez R, Camacho D (2018) Multiple classifiers in biometrics. Part 2: trends and challenges. Inf Fusion 44:103–112

    Article  Google Scholar 

  16. 16.

    Fumera G, Roli F, Giacinto G (2000) Reject option with multiple thresholds. Pattern Recognit 33:2099–2101

    Article  Google Scholar 

  17. 17.

    Gunetti D, Picardi C (2005) Keystroke analysis of free text. ACM Trans Inf Syst Secur 8(3):312–347

    Article  Google Scholar 

  18. 18.

    Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE TIFS 11(9):1984–1996

    Google Scholar 

  19. 19.

    Hammad M, Wang K (2019) Parallel score fusion of ecg and fingerprint for human authentication based on convolution neural network. Comput Secur 81:107–122

    Article  Google Scholar 

  20. 20.

    Hezil N, Boukrouche A (2017) Multimodal biometric recognition using human ear and palmprint. IET Biometr 6(5):351–359

    Article  Google Scholar 

  21. 21.

    Hossain M, Balagani K, Phoha V (2012) New impostor score based rejection methods for continuous keystroke verification with weak templates. In: IEEE conference on biometrics: theory, applications and systems (BTAS)

  22. 22.

    Hossain M, Balagani K, Phoha V (2013) On controlling genuine reject rate in multi-stage biometric verification. In: CVPRW, pp 194–199

  23. 23.

    Hossain M, Chen J, Rahman K (2018) On enhancing serial fusion based multi-biometric verification system. Appl Intell 48(12):4824–4833

    Article  Google Scholar 

  24. 24.

    Hossain MS, Rahman KA (2017) An empirical study on verifier order selection in serial fusion based multi-biometric verification system. In: IEA/AIE, pp 249–258

  25. 25.

    Huang J, Ling C (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans KDE 17(3):299–310

    Google Scholar 

  26. 26.

    Jomaa RM, Islam MS, Mathkour H (2018) Improved sequential fusion of heart-signal and fingerprint for anti-spoofing. In: Proceedings of IEEE conference on identity, security, and behavior analysis (ISBA), pp 1–7

  27. 27.

    Kasprowski P, Harezlak K (2018) Fusion of eye movement and mouse dynamics for reliable behavioral biometrics. Pattern Anal Appl 21(1):91–103

    MathSciNet  Article  Google Scholar 

  28. 28.

    Landgrebe TCW, Tax DMJ, Paclík P, Duin RPW (2006) The interaction between classification and reject performance for distance-based reject-option classifiers. Pattern Recogn Lett 27:908–917

    Article  Google Scholar 

  29. 29.

    Li JQ, Barron AR (1999) Mixture density estimation. Adv Neural Inf Process Syst 12:279–285

    Google Scholar 

  30. 30.

    Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  31. 31.

    Lumini A, Nanni L (2017) Overview of the combination of biometric matchers. Inf Fusion 33:71–85

    Article  Google Scholar 

  32. 32.

    Maity S, Abdel-Mottaleb M, Asfour S (2017) Multimodal biometrics recognition from facial video via deep learning. In: International conference on computer science, information technology and applications, pp 67–75

  33. 33.

    Marcialis G, Mastinu P, Roli F (2010) Serial fusion of multi-modal biometric systems. In: IEEE Workshop on BIOMS, pp 1 –7

  34. 34.

    Marcialis GL, Roli F, Didaci L (2009) Personal identity verification by serial fusion of fingerprint and face matchers. Pattern Recognit 42(11):2807–2817

    Article  Google Scholar 

  35. 35.

    Murakami T, Kaga Y, Takahashi K (2016) On restricting modalities in likelihood-ratio based biometric score fusion. In: ICPR, pp 3036–3042

  36. 36.

    Murakami T, Ohki T, Takahashi K (2016) Optimal sequential fusion for multibiometric cryptosystems. Inf Fusion 32:93–108

    Article  Google Scholar 

  37. 37.

    Murakami T, Takahashi K, Matsuura K (2012) Towards optimal countermeasures against wolves and lambs in biometrics. In: IEEE conference on biometrics: theory, applications and systems (BTAS), pp 69 –76

  38. 38.

    Nandakumar K, Chen Y, Dass SC, Jain A (2008) Likelihood ratio-based biometric score fusion. IEEE Trans Pattern Anal Mach Intell 30:342–347

    Article  Google Scholar 

  39. 39.

    NIST (2011) Biometric scores set. http://www.nist.gov/itl/iad/ig/biometricscores.cfm. Accessed 15 Oct 2019

  40. 40.

    Phoha V, Joshi S (2013) Method and system of identifying users based upon free text keystroke (US Patent No. 8489635, Issued July 16, 2013)

  41. 41.

    Poh N, Bourlai T, Kittler J, Allano L, Alonso-Fernandez F, Ambekar O, Baker J, Dorizzi B, Fatukasi O, Fierrez J, Ganster H, Ortega-Garcia J, Maurer D, Salah AA, Scheidat T, Vielhauer C (2009) Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms. IEEE TIFS 4(4):849–866

    Google Scholar 

  42. 42.

    Popescu-Bodorin N, Balas VE, Motoc IM (2011) 8-valent fuzzy logic for iris recognition and biometry. In: International symposium on computational intelligence and intelligent informatics (ISCIII), pp 149–154

  43. 43.

    Popescu-Bodorin N, Noaica CM, Penariu P (2015) Iris recognition with 4 or 5 fuzzy sets. In: IFSA-EUSFLAT

  44. 44.

    Raghavendra R, Dorizzi B, Rao A, Kumar GH (2011) Designing efficient fusion schemes for multimodal biometric systems using face and palmprint. Pattern Recognit 44(5):1076–1088

    Article  Google Scholar 

  45. 45.

    Raja KB, Raghavendra R, Stokkenes M, Busch C (2015) Multi-modal authentication system for smartphones using face, iris and periocular. In: International confernce on biometrics (ICB), pp 143–150

  46. 46.

    Rakhlin A, Panchenko D, Mukherjee S (2005) Risk bounds for mixture density estimation. ESAIM Probab Stat 9:220–229

    MathSciNet  Article  Google Scholar 

  47. 47.

    Ranjan R, Patel VM, Chellappa R (2019) Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE TPAMI 41(1):121–135

    Article  Google Scholar 

  48. 48.

    Sabri M, Moin M, Razzazi F (2018) A new framework for match on card and match on host quality based multimodal biometric authentication. J Sign Process Syst 91(2):163–177

    Article  Google Scholar 

  49. 49.

    Saevanee H, Clarke N, Furnell S, Biscione V (2015) Continuous user authentication using multi-modal biometrics. Comput Secur, pp 234–246

  50. 50.

    Sajjad M, Khan S, Hussain T, Muhammad K, Sangaiah AK, Castiglione A, Esposito C, Baik SW (2018) CNN-based anti-spoofing two-tier multi-factor authentication system. Pattern Recognit Lett 126:123–131

    Article  Google Scholar 

  51. 51.

    Santos-Pereira CM, Pires AM (2005) On optimal reject rules and roc curves. Pattern Recognit Lett 26:943–952

    Article  Google Scholar 

  52. 52.

    Sharma R, Das S, Joshi P (2018) Score-level fusion using generalized extreme value distribution and dsmt, for multi-biometric systems. IET Biometr 7(5):474–481

    Article  Google Scholar 

  53. 53.

    Soleymani S, Dabouei A, Kazemi H, Dawson J, Nasrabadi N (2018) Multi-level feature abstraction from convolutional neural networks for multimodal biometric identification. In: Proceedings of international conference on pattern recognition (ICPR), pp 3469–3476

  54. 54.

    Sundararajan K, Woodard DL (2018) Deep learning for biometrics: a survey. ACM Comput Surv 51(3):65:1–65:34

    Article  Google Scholar 

  55. 55.

    Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: CVPR, pp 1701–1708

  56. 56.

    Takahashi K, Mimura M, Isobe Y, Seto Y (2004) A secure and user-friendly multimodal biometric system. In: Proceedings of the SPIE, pp 12–19

  57. 57.

    Talreja V, Valenti CM, Nasrabadi N (2017) Multibiometric secure system based on deep learning. In: Proceedings of Global SIP, pp 1–5

  58. 58.

    Tortorella F (2000) An optimal reject rule for binary classifiers. In: Proceedings of the Joint IAPR Workshops, pp 611–620

  59. 59.

    Vatsa M, Singh R, Noore A (2009) Context switching algorithm for selective multibiometric fusion. In: Proceedings of international conference on pattern recognition and machine intelligence, pp 452–457

  60. 60.

    Vatsa M, Singh R, Noore A, Ross A (2010) On the dynamic selection of biometric fusion algorithms. IEEE Trans Inf Forensics Secur 5(3):470–479

    Article  Google Scholar 

  61. 61.

    Wald A (1947) Sequential analysis, 1st edn. Wiley, New York

    Google Scholar 

  62. 62.

    Yadav D, Kohli N, Agarwal A, Vatsa M, Singh R, Noore A (2018) Fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection. In: CVPR workshops

  63. 63.

    Zhang Q, Li H, Sun Z, Tan T (2018) Deep feature fusion for iris and periocular biometrics on mobile devices. IEEE TIFS 13(11):2897–2912

    Google Scholar 

  64. 64.

    Zhang Q, Yin Y, Zhan DC, Peng J (2014) A novel serial multimodal biometrics framework based on semisupervised learning techniques. IEEE Trans Inf Forensics Secur 9(10):1681–1694

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Md S. Hossain.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hossain, M.S., Balagani, K.S. & Phoha, V.V. Effectiveness of symmetric rejection for a secure and user convenient multistage biometric system. Pattern Anal Applic (2020). https://doi.org/10.1007/s10044-020-00899-0

Download citation

Keywords

  • Multi-stage biometric verification
  • Biometrics
  • Serial fusion
  • Reject option
  • Symmetric rejection
  • Sequential probability ratio test