Skip to main content
Log in

Authentication-based multimodal biometric system using exponential water wave optimization algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The biometric system relies on a single biometric identifier which could not meet the desired performance required for personal identification. Hence, identification based on the multimodal biometric system is emerged in the research community to achieve the personal identification process more effective. Owing to the strong binding among user identity and biometric template, the user privacy is revealed and hence the security resulted in a major requirement in the biometric system. An authentication based multimodal biometric system is developed in this research by considering different modalities, such as fingerprint, finger vein, and face. Here, the bit string is generated from the biometric sample in such a way that the bit strings are fused by employing the proposed Exponential Water Wave Optimization (EWWO) algorithm based on the involvement of logic operations. However, the process of fusion is accomplished in such a way that it depends on the random selection of two logic operators by the developed optimization approach. Accordingly, the developed EWWO is derived by the combination of Exponentially Weighted Moving Average (EWMA) and Water Wave Optimization (WWO) respectively. The authentication mechanism is achieved by employing the biometric template with the encoder and decoder operation. Moreover, the proposed method achieved the performance for Equal Error rate (EER), False Acceptance Rate (FAR), and False Rejection Rate (FRR) with the value of 0.0717, 0.0745, and 0.0689, respectively.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Alonso-Fernandez F, Fierrez J, Ramos D, Gonzalez-Rodriguez J (2010) Quality-based conditional processing in multi-biometrics: application to sensor interoperability. IEEE Trans Syst, Man, Cybern A 40:1168–1179. https://doi.org/10.1109/TSMCA.2010.2047498

    Article  Google Scholar 

  2. Barni M, Droandi G, Lazzeretti R, Pignata T (2019) SEMBA: secure multi-biometric authentication. IET Biom 8:411–421. https://doi.org/10.1049/iet-bmt.2018.5138

    Article  Google Scholar 

  3. Biometrics Ideal Test (2021). http://biometrics.idealtest.org/findDownloadDbByMode.do?mode=Fingerprint. Accessed 16 Apr 2021

  4. Canuto AMP, Pintro F, Xavier-Junior JC (2013) Investigating fusion approaches in multi-biometric cancellable recognition. Expert Syst Appl 40:1971–1980. https://doi.org/10.1016/j.eswa.2012.10.002

    Article  Google Scholar 

  5. Chakraborti T, McCane B, Mills S, Pal U (2018) LOOP descriptor: local optimal oriented pattern. IEEE Signal Process Lett 25:635–639. https://doi.org/10.1109/LSP.2018.2817176

    Article  Google Scholar 

  6. Chugh T, Cao K, Jain AK (2018) Fingerprint spoof buster: use of minutiae-centered patches. IEEE Trans Inform Forensic Secur 13:2190–2202. https://doi.org/10.1109/TIFS.2018.2812193

    Article  Google Scholar 

  7. Computer Vision Laboratory (2021). http://www.lrv.fri.uni-lj.si/facedb.html. Accessed 16 Apr 2021

  8. Finger Vein SDUMLA-HMT Database sample images (2021). | Download Scientific Diagram. https://www.researchgate.net/figure/Finger-Vein-SDUMLA-HMT-Database-sample-images_fig2_341907498. Accessed 6 Sep 2021

  9. Galbally J, Marcel S, Fierrez J (2014) Biometric Antispoofing methods: a survey in face recognition. IEEE Access 2:1530–1552. https://doi.org/10.1109/ACCESS.2014.2381273

    Article  Google Scholar 

  10. Gautam AK (2021) Multi-modal biometric recognition system based on FLSL fusion method and MDLNN classifier. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12(12):241–256

  11. Harikrishnan D, Sunil Kumar N, Joseph S, Nair KK (2019) Towards a fast and secure fingerprint authentication system based on a novel encoding scheme. Int J Electr Eng Educ. https://doi.org/10.1177/0020720919883803

  12. Jain AK, Nandakumar K, Nagar A (2008) Biometric template security. EURASIP J Adv Signal Process 2008:579416. https://doi.org/10.1155/2008/579416

    Article  Google Scholar 

  13. Jin ATB, Ling DNC, Goh A (2004) Biohashing: two factor authentication featuring fingerprint data and tokenised random number. Pattern Recogn 37:2245–2255. https://doi.org/10.1016/j.patcog.2004.04.011

    Article  Google Scholar 

  14. Jin Z, Jin Teoh AB, Ong TS, Tee C (2012) Fingerprint template protection with minutiae-based bit-string for security and privacy preserving. Expert Syst Appl 39:6157–6167. https://doi.org/10.1016/j.eswa.2011.11.091

    Article  Google Scholar 

  15. Kaur H, Khanna P (2019) Random distance method for generating unimodal and multimodal cancelable biometric features. IEEE Trans Inform Forensic Secur 14:709–719. https://doi.org/10.1109/TIFS.2018.2855669

    Article  Google Scholar 

  16. Kumar T (2021) An improved biometric fusion system of fingerprint and face using whale optimization. Int J Adv Comput Sci Appl 12:1. https://doi.org/10.14569/IJACSA.2021.0120176

    Article  Google Scholar 

  17. Lakshmi Priya B, Pushpa Rani M (2020) A multimodal biometric user verification system with identical twin using SVM2. 8:5. International Journal of Recent Technology and Engineering (IJRTE):2277–3878. https://doi.org/10.35940/ijrte.E6805.038620

  18. Leng L (2011) Dual-key-binding cancelable palmprint cryptosystem for palmprint protection and information security. J Netw Comput Appl 34:1979–1989. https://doi.org/10.1016/j.jnca.2011.07.003

    Article  Google Scholar 

  19. Leng L, Zhang J (2013) Palmhash code vs. palmphasor code. Neurocomputing 108:1–12. https://doi.org/10.1016/j.neucom.2012.08.028

  20. Leng L (2015) Alignment-free row-co-occurrence cancelable palmprint fuzzy vault. Pattern Recogn 48:2290–2303. https://doi.org/10.1016/j.patcog.2015.01.021

    Article  Google Scholar 

  21. Leng L (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76:333–354. https://doi.org/10.1007/s11042-015-3058-7

    Article  Google Scholar 

  22. Leng L, Li M, Teoh ABJ (2013, December) Conjugate 2DPalmHash code for secure palm-print-vein verification. In: 2013 6th International congress on image and signal processing (CISP). IEEE, vol. 3, pp 1705–1710. https://doi.org/10.1109/CISP.2013.6743951

  23. Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010, November) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In: 2010 international conference on information and communication technology convergence (ICTC). IEEE, pp 467–471. https://doi.org/10.1109/ICTC.2010.5674791

  24. Leng L, Teoh ABJ, Li M, Khan MK (2013) A remote cancelable palmprint authentication protocol based on multidirectional twodimensional. PalmPhasor-fusion 7:1860–1871. https://doi.org/10.1002/sec.900

    Article  Google Scholar 

  25. Mustafa AS, Abdulelah AJ (2020) Multimodal biometric system Iris and fingerprint recognition based on fusion technique. Int J Adv Sci Technol 29:7423–7432

    Google Scholar 

  26. Poh N, Kittler J, Bourlai T (2010) Quality-based score normalization with device qualitative information for multimodal biometric fusion. IEEE Trans Syst Man Cybern A 40:539–554. https://doi.org/10.1109/TSMCA.2010.2041660

    Article  MATH  Google Scholar 

  27. Purohit H, Ajmera PK (2021) Optimal feature level fusion for secured human authentication in multimodal biometric system. Mach Vis Appl 32. https://doi.org/10.1007/s00138-020-01146-6

  28. Ratha NK, Chikkerur S, Connell JH, Bolle RM (2007) Generating cancelable fingerprint templates. IEEE Trans Pattern Anal Mach Intell 29:561–572. https://doi.org/10.1109/TPAMI.2007.1004

    Article  Google Scholar 

  29. Rathgeb C, Breitinger F, Busch C (2013) Alignment-free cancelable iris biometric templates based on adaptive bloom filters. In: 2013 international conference on biometrics (ICB). IEEE, Madrid, pp 1–8. https://doi.org/10.1109/ICB.2013.6612976

    Chapter  Google Scholar 

  30. Saccucci MS, Amin RW, Lucas JM (1992) Exponentially weighted moving average control schemes with variable sampling intervals. Commun Stat-Simul Comput 21:627–657. https://doi.org/10.1080/03610919208813040

    Article  MathSciNet  Google Scholar 

  31. Sadhya D, Singh SK (2018) Construction of a Bayesian decision theory-based secure multimodal fusion framework for soft biometric traits. IET Biom 7:251–259. https://doi.org/10.1049/iet-bmt.2017.0049

    Article  Google Scholar 

  32. Sultana M, Paul PP, Gavrilova ML (2018) Social behavioral information fusion in multimodal biometrics. IEEE Trans Syst Man Cybern, Syst 48:2176–2187. https://doi.org/10.1109/TSMC.2017.2690321

    Article  Google Scholar 

  33. Tomar P, Singh RC (2021) Cascade-based multimodal biometric recognition system with fingerprint and face. Macromol Symp 397:1. https://doi.org/10.1002/masy.202000271

    Article  Google Scholar 

  34. Veluchamy S, Karlmarx LR (2017) System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier. IET Biom 6:232–242. https://doi.org/10.1049/iet-bmt.2016.0112

    Article  Google Scholar 

  35. Vhaduri S, Poellabauer C (2019) Multi-modal biometric-based implicit authentication of wearable device users. IEEE Trans Inform Forensic Secur 14:3116–3125. https://doi.org/10.1109/TIFS.2019.2911170

    Article  Google Scholar 

  36. Walia GS, Rishi S, Asthana R, Kumar A, Gupta A (2019) Secure multimodal biometric system based on diffused graphs and optimal score fusion. IET Biom 8:231–242. https://doi.org/10.1049/iet-bmt.2018.5018

    Article  Google Scholar 

  37. Walia GS, Jain G, Bansal N, Singh K (2020) Adaptive weighted graph approach to generate multimodal cancelable biometric templates. IEEE Trans Inform Forensic Secur 15:1945–1958. https://doi.org/10.1109/TIFS.2019.2954779

    Article  Google Scholar 

  38. Xin Y, Kong L, Liu Z, Wang C, Zhu H, Gao M, Zhao C, Xu X (2018) Multimodal feature-level fusion for biometrics identification system on IoMT platform. IEEE Access 6:21418–21426. https://doi.org/10.1109/ACCESS.2018.2815540

    Article  Google Scholar 

  39. Xiong Q, Zhang X, Xu X, He S (2021) A modified chaotic binary particle swarm optimization scheme and its application in face-iris multimodal biometric identification. Electronics 10:1. https://doi.org/10.3390/electronics10020217

    Article  Google Scholar 

  40. Yang W, Wang S, Hu J, Zheng G, Valli C (2018) A fingerprint and finger-vein based cancelable multi-biometric system. Pattern Recogn 78:242–251. https://doi.org/10.1016/j.patcog.2018.01.026

    Article  Google Scholar 

  41. Zheng Y-J (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11. https://doi.org/10.1016/j.cor.2014.10.008

    Article  MathSciNet  MATH  Google Scholar 

  42. Zhong D, Shao H, Du X (2019) A hand-based multi-biometrics via deep hashing network and biometric graph matching. IEEE TransI nform Forensic Secur 14:3140–3150. https://doi.org/10.1109/TIFS.2019.2912552

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vensila C.

Ethics declarations

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

C, V., Wesley, A.B. Authentication-based multimodal biometric system using exponential water wave optimization algorithm. Multimed Tools Appl 82, 30275–30307 (2023). https://doi.org/10.1007/s11042-023-14498-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14498-8

Keywords

Navigation