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Quality assessment-based iris and face fusion recognition with dynamic weight

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Abstract

Image quality is one of the most crucial influence factors when conducting biometric image-based human identification. However, it is not considered in most existing multi-biometric fusion algorithms. In this paper, a quality assessment-based dynamic weighted fusion algorithm is proposed, which applies a method of using image quality scores, and the scores are evaluated by integrating various image quality metrics to assess the quality of the feature matching process. According to the classification of the dual-modality feature matching quality, a dynamic weighted fusion strategy is proposed to increase the weight of biometric traits with better quality and weaken the impact of low-quality biometric traits on the recognition results, to achieve the adaptive optimization fusion of the biometric score level. Finally, the fused scores are used to make decisions. Experimental results reveal that the proposed algorithm is more robust and has better achievement than unimodal biometrics and traditional fusion algorithms.

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References

  1. Dinca, L.M., Hancke, G.: The fall of one, the rise of many: a survey on multi-biometric fusion methods. IEEE Access 5(99), 6247–6289 (2017)

    Article  Google Scholar 

  2. Gupta, K, Walia, G.S, Sharma K.: Novel approach for multimodal feature fusion to generate cancelable biometric. Vis. Comput. 1–13 (2020)

  3. Kihal, N., Chitroub, S., Meunier, J.: Fusion of iris and palmprint for multimodal biometric authentication. In: International Conference on Image Processing Theory, Tools and Applications, IEEE, pp. 1–6 (2014)

  4. Eskandari, M., Sharifi, O.: Optimum scheme selection for face-iris biometric. Iet Biometrics 6(5), 334–341 (2017)

    Article  Google Scholar 

  5. Liau, H.F., Isa, D.: Feature selection for support vector machine-based face-iris multimodal biometric system. Expert Syst. Appl. 38(9), 11105–11111 (2011)

    Article  Google Scholar 

  6. Sim, H.M., Asmuni, H., Hassan, R., Othman, R.M.: Multimodal biometrics:weighted score level fusion based on non-ideal iris and face images. Expert Syst. Appl. 41(11), 5390–5404 (2014)

    Article  Google Scholar 

  7. Burge, M.J., Bowyer, K.W.: Handbook of Iris Recognition. Springer, London (2013)

    Book  Google Scholar 

  8. Wang, Z., Yang, J., Wang, E., Liu, Y., Ding, Q.: A novel multimodal biometric system based on iris and face. Int. J. Digit. Content Technol. Appl. 6(2), 111–118 (2012)

    Google Scholar 

  9. Miao, D., Sun, Z., Huang, Y.: Fusion of Multibiometrics Based on a New Robust Linear Programming. In: International Conference on Pattern Recognition, IEEE, pp. 291–296 (2014)

  10. Ross, A.A.: Handbook of Multibiometrics. Springer, Berlin (2006)

    Google Scholar 

  11. Matin, A., Mahmud, F., Ahmed, T., Ejaz, M. S.: Weighted score level fusion of iris and face to identify an individual. In: 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE, pp. 1–4 (2017)

  12. Anzar, S,M., Sathidevi, P.S.: Optimization of integration weights for a multibiometric system with score level fusion. In: Advances in Computing and Information Technology, Springer Berlin Heidelberg, pp. 833–842 (2013)

  13. Murakami, T., Takahashi, K.: Fast and accurate biometric identification using score level indexing and fusion. In: International Joint Conference on Biometrics, IEEE Computer Society, pp. 1–8 (2011)

  14. Miao, D., Zhang, M., Sun, Z., Tan, T., He, Z.: Bin-based classifier fusion of iris and face biometrics. Neurocomputing 224(8), 105–118 (2017)

    Article  Google Scholar 

  15. Bouzouina, Y., Hamami, L.: Multimodal biometric: Iris and face recognition based on feature selection of iris with GA and scores level fusion with SVM. In: International Conference on Bio-Engineering for Smart Technologies, pp. 1–7 (2017)

  16. Azom, V., Adewumi, A., Tapamo, J.R.: Face and Iris biometrics person identification using hybrid fusion at feature and score-level. In: Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, IEEE, pp. 207–212 (2015)

  17. Abaza, A, Harrison, M,A., Bourlai, T.: Quality metrics for practical face recognition. In: International Conference on Pattern Recognition, IEEE, pp. 3103–3107 (2013)

  18. Phillips, P.J., Beveridge, J,R., Bolme, D.S., Draper, B,A., Zhang,H.: On the existence of face quality measures. In: IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems, IEEE, pp. 1–8 (2013)

  19. Yuling, R., Wen, L.U., Hongqiang, X.U., Lihuo, H.E.: An image quality assessment method based on shearlet transform. Journal of Shandong University (Engineering Science) (2015)

  20. Guofeng, Z., Guixia, F.U., Zhenmei, L.I., Haitao, L.I., Kejun, W.: Face image quality evaluation method based on the fusion of two level evaluation indexes. Journal of Shandong University (Engineering Science) (2016)

  21. Wan, J., He, X.F., Shi, P.: An iris image quality assessment method based on laplacian of gaussian operation. In: IAPR Conference on Machine Vision Applications, DBLP, pp. 248–251 (2017)

  22. Jang, J., Park, K.R., Kim, J., Lee, Y.: New focus assessment method for iris recognition systems. Pattern Recognit. Lett. 29(13), 1759–1767 (2008)

    Article  Google Scholar 

  23. Liu, X., Pedersen, M., Charrier, C.: Image-based attributes of multi-modality image quality for contactless biometric samples. In: International Conference on Signal Processing and Integrated Networks. IEEE, pp. 106–111(2016)

  24. Kamble, V., Bhurchandi, K.M.: No-reference image quality assessment algorithms: A survey. Optik 126(11–12), 1090–1097 (2015)

    Article  Google Scholar 

  25. Zhu, T., Karam, L.: A no-reference objective image quality metric based on perceptually weighted local noise. Eurasip J. Image Video Process. 1(5), 1–8 (2014)

    Google Scholar 

  26. Hosseini, M.S., Plataniotis, K.N.: No-reference image quality assessment and blind deblurring with sharpness metrics exploiting fourier phase information. J. Math. Imag. Vis. 52(1), 1–28 (2015)

    Article  MathSciNet  Google Scholar 

  27. Merati, A., Poh, N., Kittler, J.: User-specific cohort selection and score normalization for biometric systems. IEEE Trans. Inf. Forens. Secur. 7(4), 1270–1277 (2012)

    Article  Google Scholar 

  28. Zhou, W., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  29. Sutra, G., Garcia-Salicetti, S., Dorizzi, B.: The viterbi algorithm at different resolutions for enhanced iris segmentation. In: IAPR International Conference on Biometrics (ICB), IEEE, pp. 310–316 (2012)

  30. Masek, L.: Recognition of human iris patterns for biometric identification (2003)

  31. Chinese Academy of Science-Institute of Automation, CASIA-IRIS-V2 Iris Image Database Version2.0 (CASIA-IRIS-V2-Iris V2)[Online]. http://biometrics.idealtest.org/dbDetailForUser.do?id=2.

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Acknowledgements

This work was supported by the National Key R&D Program of China under Grant 2017YFB0802300.

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, China

    Ke Xiao, Yutong Tian, Yuanyao Lu, Yuping Lai & Xunchang Wang

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  1. Ke Xiao
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  2. Yutong Tian
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  3. Yuanyao Lu
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  4. Yuping Lai
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  5. Xunchang Wang
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Correspondence to Yutong Tian.

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Xiao, K., Tian, Y., Lu, Y. et al. Quality assessment-based iris and face fusion recognition with dynamic weight. Vis Comput 38, 1631–1643 (2022). https://doi.org/10.1007/s00371-021-02093-7

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