Skip to main content

Advertisement

SpringerLink
Log in

Optimal feature level fusion for secured human authentication in multimodal biometric system

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

The rising demand for high security and reliable authentication schemes, led to the development of the unimodal biometric system so that the multimodal biometric system has emerged. The multimodal biometric system will use more than one biometric trait of an individual for identification and security purpose. Fusion plays a major role in the multimodal biometric system. Several fusion techniques are used in biometric systems. Feature level fusion is a very much popular method as compared to the other fusion techniques. In this fusion, features are extracted from all biometric traits. After that extracted features are combined into a final feature vector of high dimension. In this paper, we introduce a new technique to perform fusion at the feature level by optimal feature level fusion; here the relevant features are selected using an optimization technique. Here, we proposed OGWO for selecting optimal features. Moreover, we suggested the recognition technique. For recognition, we use the multi-kernel support vector machine algorithm. Finally, the performance of our proposed method is evaluated by some evaluation measures. Our recommended method is implemented in the MATLAB platform.

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.

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

Similar content being viewed by others

References

  1. Kazimov, T., Mahmudova, S.: The role of biometric technology in information security. Int. Res. J. Eng. Technol. (IRJET) 2(03), 1509–1513 (2015)

    Google Scholar 

  2. George, A., Routray, A.: A score level fusion method for eye movement biometrics. Pattern Recogn. Lett. 82, 207–215 (2016)

    Article  Google Scholar 

  3. Singh, R., Ross, A., Bowyer, K.W.: Special issue on information fusion in biometrics. Inform. Fusion 32, 1–2 (2016)

    Article  Google Scholar 

  4. Panchal, T., Singh, A.: Multimodal biometric system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(5), 1360–1363 (2013)

    Google Scholar 

  5. Yang, W., Hu, J., Wang, S., Chen, C.: Mutual dependency of features in multimodal biometric systems. Electron. Lett. 51(3), 234–235 (2015)

    Article  Google Scholar 

  6. Mai, G., Lim, M.-H., Yuen, P.C.: Binary feature fusion for discriminative and secure multi-biometric cryptosystems. Image Vis. Comput. 58, 254–265 (2017)

    Article  Google Scholar 

  7. Peng, J., El-Latif, A.A.A., Li, Q., Niu, X.: Multimodal biometric authentication based on score level fusion of finger biometrics. Optik Int. J. Light Electron. Opt. 125(23), 6891–6897 (2014)

    Article  Google Scholar 

  8. Geethanjali, N., Thamaraiselvi, K.: Feature level fusion of multimodal biometrics and two tier security in ATM system. Int. J. Comput. Appl. 70(14), 17–23 (2013)

    Google Scholar 

  9. Bhardwaj, S.K.: An algorithm for feature level fusion in multimodal biometric system. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 3, 3499–3503 (2014)

    Google Scholar 

  10. Jacob, A.J., Bhuvan, N.T., Thampi, S.M.: Feature level fusion using multiple fingerprints. Comput. Sci. New Dimens. Perspect. 4(1), 13–18 (2011)

    Google Scholar 

  11. Ahmad, M.I., Woo, W.L., Dlay, S.: Non-stationary feature fusion of face and palmprint multimodal biometrics. NeuroComput. 177, 49–61 (2016)

    Article  Google Scholar 

  12. Mondal, A., Kaur, A.: Comparative study of feature level and decision level fusion in multimodal biometric recognition of face ear and iris. Int. J. Comput. Sci. Mob. Comput. (IJCSMC) 5(5), 822–842 (2016)

    Google Scholar 

  13. Xin, M., Xiaojun, J.: Correlation-based identification approach for multimodal biometric fusion. J. China Univ. Posts Telecommun. 24(4), 34–50 (2017)

    Article  Google Scholar 

  14. Yuan, Y.-H., Sun, Q.-S., Zhou, Q., Xia, D.-S.: A novel multi set integrated canonical correlation analysis framework and its application in feature fusion. Pattern Recognit. 44(5), 1031–1040 (2011)

    Article  Google Scholar 

  15. Sarier, N.D.: Multimodal biometric identity based encryption. Fut. Gener. Comput. Syst. 80, 1–17 (2018)

    Article  Google Scholar 

  16. Gao, X., Sun, Q., Xu, H.: Multiple-rank supervised canonical correlation analysis for feature extraction, fusion and recognition. Expert Syst. Appl. 84, 171–185 (2017)

    Article  Google Scholar 

  17. Yang, J., Zhang, X.: Feature-level fusion of fingerprint and finger-vein for personal identification. Pattern Recognit. Lett. 33(5), 623–628 (2012)

    Article  Google Scholar 

  18. Veluchamy, S., Karlmarx, L.R.: 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(3), 232–242 (2016)

    Article  Google Scholar 

  19. Nagar, A., Nandakumar, K., Jain, A.K.: Multibiometric cryptosystems based on feature-level fusion. IEEE Trans. Inform. Forens. Secur. 7(1), 255–268 (2012)

    Article  Google Scholar 

  20. Yang, W., Wang, S., Hu, J., Zheng, G., Valli, C.: A fingerprint and finger-vein based cancelable multi-biometric system. Pattern Recognit. 78, 242–251 (2018)

    Article  Google Scholar 

  21. Slanzi, G., Pizarro, G., Velásquez, J.D.: Biometric information fusion for web user navigation and preferences analysis: an overview. Inform. Fusion 38, 12–21 (2017)

    Article  Google Scholar 

  22. Miao, D., Zhang, M., Sun, Z., Tan, T., He, Z.: Bin-based classifier fusion of iris and face biometrics. Neuro Comput. 224, 105–118 (2017)

    Google Scholar 

  23. Purohit, H., Ajmera, P. K.: Multimodal multilevel fusion of face ear iris with multiple classifications. In: International conference on Modelling, Simulation and Intelligent Computing (pp. 345–355). Springer, Singapore (2020)

  24. Purohit, H., Ajmera, P.: Automated person authentication using face, iris and ear multimodal biometric fusion. In: ICT for Competitive Strategies (pp. 511–518). CRC Press (2020)

  25. Purohit, H., Ajmera, P. K.: Contemporary biometric system design. In: Handbook of Research on Engineering Innovations and Technology Management in Organizations (pp. 292–324). IGI Global (2020)

  26. Purohit, H., Ajmera, P. K.: Fusions of Palm Print with Palm-Phalanges Print and Palm Geometry. In: International Conference on Advanced Computing Networking and Informatics (pp. 553–560). Springer, Singapore (2019)

  27. Purohit, H., Ajmera, P. K..: Multimodal biometric systems, a brief study. In: International Journal of Innovative Technology and Exploring Engineering

  28. Sinha, H., Ajmera, P. K.: Upgrading security and protection in ear biometrics. IET Biometrics 8(4), 259–266 (2019)

  29. Sinha, H., Awasthi, V., Ajmera, P. K.: Audio classification using braided convolutional neural networks. IET Signal Processing 14(7), 448–454 (2020)

  30. Duan, M., Li, K., Liao, X., Li, K., Tian, Q.: Features-enhanced multi-attribute estimation with convolutional tensor correlation fusion network. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 15(3), 1–23 (2019)

    Google Scholar 

  31. Duan, M., Li, K., Li, K.: An ensemble cnn2elm for age estimation. IEEE Trans. Inf. Forens. Secur. 13(3), 758–772 (2017)

    Article  Google Scholar 

  32. Duan, M., Li, K., Yang, C., Li, K.: A hybrid deep learning CNN–ELM for age and gender classification. Neurocomputing 275, 448–461 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, BITS PILANI, Pilani, India

    Himanshu Purohit & Pawan K. Ajmera

Authors
  1. Himanshu Purohit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pawan K. Ajmera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Himanshu Purohit.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Purohit, H., Ajmera, P.K. Optimal feature level fusion for secured human authentication in multimodal biometric system. Machine Vision and Applications 32, 24 (2021). https://doi.org/10.1007/s00138-020-01146-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01146-6

Keywords

Search

Navigation