Skip to main content
SpringerLink
Log in

Feature extractor selection for face–iris multimodal recognition

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Multimodal biometrics-based systems aim to improve the recognition accuracy of human beings using more than one physical and/or behavioral characteristics of a person. In this paper, different fusion schemes at matching score level and feature level are employed to obtain a robust recognition system using several standard feature extractors. The proposed method involves the consideration of a face–iris multimodal biometric system using score level and feature level fusion. Principal Component Analysis (PCA), subspace Linear Discriminant Analysis (LDA), subpattern-based PCA, modular PCA and Local Binary Patterns (LBP) are global and local feature extraction methods applied on face and iris images. In fact, different feature sets obtained from five local and global feature extraction methods for unimodal iris biometric system are concatenated at feature level fusion called iris feature vector fusion (iris-FVF), while for unimodal face biometric system, LBP is used to achieve efficient texture descriptors. Feature selection is performed using Particle Swarm Optimization (PSO) at feature level fusion step to reduce the dimension of feature vectors for improving the recognition performance. Our proposed method is validated by forming three datasets using ORL, BANCA, FERET face databases and CASIA, UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed matching score level fusion scheme using Weighted Sum rule, tanh normalization, iris-FVF and facial features extracted by LBP achieves a significant improvement over unimodal and multimodal methods. Support Vector Machine (SVM) and t-norm normalization are also used to improve the recognition performance of the proposed method.

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

Similar content being viewed by others

References

  1. Wang, N., Li, Q., Han, Q.: A novel multi-division template protection (MDTP) scheme for iris recognition based on fuzzy vault. In: Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 101–104 (2011)

  2. Liau, H.F., Isa, D.: Feature selection for support vector machine-based face-iris multimodal biometric system. Expert Syst. Appl. 38, 11105–11111 (2011)

    Article  Google Scholar 

  3. Proenca, H.P.: Towards non-cooperative biometric iris recognition. PhD thesis, submitted to University of Beira Interior Department of Computer Science, October 2006

  4. Gomez-Barrero, M., Galbally, J., Fierrez”, J.: Efficient software attack to multimodal biometric systems and its application to face and iris fusion. Pattern Recogn. Lett. 36, 243–253 (2014)

    Article  Google Scholar 

  5. Kim, Y.G., Shin, K.Y., Leek, E.C., Park, K.R.K.: Multimodal biometric system based on the recognition of face and both irises. Int. J. Adv. Robot. Syst. 9(65), 1–6 (2012)

  6. Karmakar, D., Murthy, C.A.: Generation of new points for training set and feature-level fusion in multimodal biometric identification. Mach. Vis. Appl. 25(2), 477–487 (2014)

  7. Eskandari, M., Toygar, Ö., Demirel, H.: A new approach for face-iris multimodal biometric recognition using score fusion. Int. J. Pattern Recogn. Artif. Intell. 27(3), 1–15 (2013)

    Article  MathSciNet  Google Scholar 

  8. Wang, N., Li, Q., Abd El-Latif, A. A., Peng, J.L., Niu, X.: Multibiometrics fusion for identity authentication: dual iris, visible and thermal face imagery. Int. J. Secur. Appl., 7(3) (2013)

  9. Woodard, D.L., Pundlik, S.J., Miller, P.E., Lyle, J.R.: Appearance-based periocular features in the context of face and non-ideal iris recognition. SIViP 5(4), 443–455 (2011)

  10. Monwar, M.M., Gavrilova, M.: Markov chain model for multimodal biometric rank fusion. SIViP 7(1), 137–149 (2013)

  11. Raghavendra, R., Dorizzi, B., Rao, A., Kumar, G.H.: Designing efficient fusion schemes for multimodal biometric system using face and palmprint. Pattern Recogn. 44, 1076–1088 (2011)

    Article  MATH  Google Scholar 

  12. Ross, A.A., Jain, A.K., Nandakumar, K.: Handbook of multibiometrics, International Series on Biometrics vol. 6. Springer (2006)

  13. Toygar, Ö., Altınçay, H.: Preserving spatial information and overcoming variations in appearance for face recognition. Pattern Anal. Appl. 14(1), 67–75 (2011)

    Article  MathSciNet  Google Scholar 

  14. Chen, S., Zhu, Y.: Subpattern-based principle component analysis. Pattern Recogn. 37, 1081–1083 (2004)

    Article  Google Scholar 

  15. Gottumukkal, R., Asari, V.K.: An improved face recognition technique based on modular PCA approach. Pattern Recogn. Lett. 25, 429–436 (2004)

    Article  Google Scholar 

  16. Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  17. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognit. Neurosci. 3(1), 71–96 (1991)

    Article  Google Scholar 

  18. Xiaogang, W., Xiaoou, T.: Random sampling for subspace face recognition. Int. J. Comput. Vis. 70(1), 91–104 (2006)

    Article  Google Scholar 

  19. AT&T Laboratories Cambridge, the ORL Database of Faces (ORL). Retrieved from 2009. http://www.camorl.co.uk/facedatabase.html

  20. Bailly-B, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Mariethoz, J., Matas, J., Messer, K., Popovici, V., Poree, F., Ruiz, B., Thiran, J.P.: The BANCA database and evaluation protocol. In: audio and video-based biometric person authentication: Proc. 4th Int. Conf., AVBPA2003, LNCS 2688, pp 625–638, Germany (2003)

  21. UBIRIS iris database, Retrieved 2009 from http://iris.di.ubi.pt

  22. CASIA-IrisV3, Retrieved 2009, from http://www.cbsr.ia.ac.cn/IrisDatabase.htm

  23. Philips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image Vis. Comput. 16, 295–306 (1998)

    Article  Google Scholar 

  24. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. Perth Aust. 4(2), 1942–1948 (1995)

    Google Scholar 

  25. Eskandari, M., Toygar, Ö.: Fusion of face and iris biometrics using local and global feature extraction methods. SIViP (2012). doi:10.1007/s11760-012-0411-4

  26. Wang, N., Li, Q., Abd El-Latif, A.A., Zhang, T., Niu, X.: Toward accurate localization and high recognition performance for noisy iris images. Multimed Tools Appl., 2012. doi:10.1007/s11042-012-1278-7

  27. Puhan, N.B., Sudha, N., Kaushalram, A.S.: Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density. SIViP 5(1), 105–119 (2011)

  28. Türkan, M., Pardas, M., Çetin, A.E.: Human eye localization using edge projections. VISAPP (1), pp. 410–415 (2007)

  29. Lajevardi, S.M., Hussain, Z.M.: Automatic facial expression recognition: feature extraction and selection. SIViP 6(1), 159–169 (2012)

  30. Drygajlo, A., Li, W.: Client-specific A-stack model for adult face verification across aging. Signal, Image Video Process., Springer, London 5(4), 231–441 (2011)

  31. Türkan, M., Dülek, B., Onaran, I., Çetin, A.E.: Human face detection in video using edge projections. In: International Society for Optics and Photonics, Defense and Security Symposium, pp. 624607–624607 (2006)

  32. Pujol, P., Macho, D., Nadeu, C.: On real-time mean-and- variance normalization of speech recognition feature. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006

  33. Torch3vision, Retrieved, July 2011, from http://torch3vision.idiap.ch

  34. Wang, F., Han, J.: Multimodal biometric authentication based on score level fusion using support vector machine. Opto-Electron. Rev. 17(1), 59–64 (2009)

    Article  Google Scholar 

  35. Jain, A.K., Ross, A.: Learning User-specific Parameters in a Multibiometric System. In: Proceedings of International Conference on Image Processing, New York, USA, September 2002, pp. 57–60

  36. Jain, A., Nandakumar, K., Ross, A.: Score Normalization in multimodal biometric systems. Pattern Recogn. 38, 2270–2285 (2005)

    Article  Google Scholar 

  37. Auckenthaler, R., Care, M., Lloyd-Thomas, H.: Score normalization for text-independent speaker verification systems. Digit Signal Process. 10(1), 42–54 (2000)

    Article  Google Scholar 

  38. Chen, C.H., Te Chu, C.: Fusion of face and iris features for multimodal biometrics. International Conference on Advances in Biometrics, ICB 2006, LNCS, vol. 3832, pp. 571–580, Hong Kong, China (2006)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Eastern Mediterranean University, Gazimagusa, Northern Cyprus, Mersin 10, Turkey

    Maryam Eskandari & Önsen Toygar

  2. Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimagusa, Northern Cyprus, Mersin 10, Turkey

    Hasan Demirel

Authors
  1. Maryam Eskandari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Önsen Toygar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hasan Demirel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryam Eskandari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eskandari, M., Toygar, Ö. & Demirel, H. Feature extractor selection for face–iris multimodal recognition. SIViP 8, 1189–1198 (2014). https://doi.org/10.1007/s11760-014-0659-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0659-y

Keywords

Search

Navigation