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Score level multibiometrics fusion approach for healthcare

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Abstract

Biometric traits such as iris and face can help for the elementary assessment of human diseases and healthcare monitoring. It has several advantages such as increased patient and staff (doctors and nurses) safety, the accuracy and quality of the healthcare system, reduction of the healthcare fraud. In addition, it provides a secure way to detect the inhabitant’s mood and ocular pathologies in order to treat them. The paper introduces a prototype for biometric-based healthcare monitoring. In the proposed prototype, the patient/user seeking for healthcare assistance can send a request by his/her biometric traits. The biometric traits are processed in the cloud management. The caregiver with valid identification/verification can receive the request and analyze it in order for further treatment. This paper also introduces an efficient multibiometrics fusion framework based on Aczél-Alsina triangular norm. The proposed approach utilizes the 1D-log Gabor iris features, two-directional two-dimensional modified fisher principal component analysis (\((2\hbox {D})^{2}\)MFPCA) and Complex Gabor Jet Descriptor face features to be used for healthcare monitoring. Results show that the multibiometrics fusion approach has better performance compared with the previous fusion approaches.

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References

  1. Trokielewicz, M., Czajka, A., Maciejewicz, P.: Implications of ocular pathologies for iris recognition reliability. Imag. Vis. Comput. 58, 158–167 (2017)

    Article  Google Scholar 

  2. Alhussein, M.: Automatic facial emotion recognition using weber local descriptor for e-Healthcare system. Clust. Comput. 19(1), 99–108 (2016)

    Article  Google Scholar 

  3. Hossain, M.S.: Cloud-supported cyber-physical localization framework for patients monitoring. IEEE Syst. J. 11(1), 118–127 (2017)

    Article  MathSciNet  Google Scholar 

  4. Muhammad, G.: Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Clust. Comput. 18(2), 795–802 (2015)

    Article  MathSciNet  Google Scholar 

  5. Hossain, M.S., Muhammad, G.: Cloud-assisted industrial internet of things (IIoT)-enabled framework for health monitoring. Comput. Netw. 101, 192–202 (2016)

    Article  Google Scholar 

  6. Hossain, M.S., Muhammad, G., Alhamid, M.F., Song, B., Almutib, K.: Audio-visual emotion recognition using big data towards 5G. Mob. Netw. Appl. 21(5), 753–763 (2016)

    Article  Google Scholar 

  7. Hu, Y., et al.: Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics, Springer, New York (June 2016)

  8. Wang, Y., Tan, T., Jain, A.K.: Combining face and iris biometrics for identity verification. Lect. Notes Comput. Sci. 2688, 805–813 (2003)

    Article  MATH  Google Scholar 

  9. Choi, J., Hu, S., Young, S.S., Davis, L.S.: Thermal to visible face recognition, In: Proceedings SPIE 8371, United States, pp. 1–11 (2002)

  10. Choi, J., Dixon, K.R., Wick, D.V., Bagwell, B.E., Soehnel, G.H., Clark, B.: Iris imaging system with adaptive optical elements. J. Electron. Imaging 21(1), 013004 (2012)

    Article  Google Scholar 

  11. Yang, G., Xi, X., Yin, Y.: Finger vein recognition based on a personalized best bit map. Sensors 12(2), 1738–1757 (2012)

    Article  Google Scholar 

  12. Zhang, D., Lu, G.: 3D Palmprint Capturing System. In: 3D Biometrics, Springer, New York, pp. 85–104 (2013)

  13. Hu, L., Qiu, M., Song, J., Shamim Hossain, M., Ghoneim, A.: Software defined healthcare networks. IEEE Wirel. Commun. 22(6), 67–75 (2015)

    Article  Google Scholar 

  14. Chan, C.H., Goswami, B., Kittler, J., Christmas, W.: Local ordinal contrast pattern histograms for spatiotemporal, lip-based speakerauthentication. IEEE Trans. Inf. Forensics Secur. 7(2), 6002–612 (2012)

    Article  Google Scholar 

  15. Sun, X., Wang, G., Wang, L., Sun, H., Wei, X.: 3D ear recognition using local salience and principal manifold. Graphical Models 76(5), 402–412 (2014)

    Article  Google Scholar 

  16. Hossain, M.S., El Saddik, A.: A biologically inspired multimedia content repurposing system in heterogeneous environments. Multimed. Syst. J. 14(3), 135–143 (2008)

    Article  Google Scholar 

  17. Hossain, M.S., Muhammad, G., Rahman, S.M.M., Abdul, W., Alelaiwi, A., Almari, A.: Toward end-to-end biomet rics-based security for IoT infrastructure. IEEE Wirel. Commun. Mag. 23(5), 45–51 (2016)

    Article  Google Scholar 

  18. Bian, W., Ding, S., Xue, Y.: Combining weighted linear project analysis with orientation diffusion for fingerprint orientation field reconstruction. Inf. Sci. 396, 55–71 (2017)

    Article  Google Scholar 

  19. Galbally, J., McCool, C., Fierrez, J., Marcel, S., Ortega-Garcia, J.: On the vulnerability of face verification systems to hill-climbing attacks. Pattern Recognit. 43(3), 1027–1038 (2010)

    Article  MATH  Google Scholar 

  20. Wang, G., Wu, H.: Research and realization on voice restoration technique for voice communication software, In: Proceedings of International Symposium on Information Engineering and Electronic Commererce, Ternopil, Ukraine, pp. 791–795 (2009)

  21. Venugopalan, S., Savvides, M.: How to generate spoofed irises from an iris code template. IEEE Trans. Inf. Forensic Secur. 6(2), 385–395 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Kang, B.J., Park, K.R., Yoo, J.-H., Kim, J.N.: Multimodal biometric method that combines veins, prints, and shape of a finger. Opt. Eng. 50(1), 017201 (2011)

    Article  Google Scholar 

  25. Chang, K.I., Bowyer, K.W., Flynn, P.J., Chen, X.: Multi-biometrics using facial appearance, shape and temperature, In: Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Republic of Korea, pp. 43–48 (2012)

  26. Tchamova, A., Dezert, J., Smarandache, F.: A new class of fusion rules based on T conorm and T norm fuzzy operators. Inf. Secur. 20, 65–82 (2006)

    MathSciNet  Google Scholar 

  27. Wu, H., Siegel, M., Stiefelhagen, R., Yang, J.: Sensor fusion using Dempster-Shafer theory, In: Proceedings of the 19th IEEE Conference on Instrumentation and Measurement Technology, vol. 1, pp. 7–11, Anchorage, AK, United States (2002)

  28. Quost, B., Masson, M.-H., Denoeux, T.: Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules. Int. J. Approx. Reason. 52(3), 353–374 (2011)

    Article  MathSciNet  Google Scholar 

  29. Hanmandlu, M., Grover, J., Gureja, A., Gupta, H.: Score level fusion of multimodal biometrics using triangular norms. Pattern Recognit. Lett. 32(14), 1843–1850 (2011)

    Article  Google Scholar 

  30. Srivastava, S., Bhardwaj, S., Bhargava, S.: Fusion of palm-phalanges print with palmprint and dorsal hand vein. Appl. Soft Comput. 47, 12–20 (2016)

    Article  Google Scholar 

  31. Wang, N., Lu, L., Gao, G., Wang, F., Li, S.: Multibiometrics fusion using Aczél-Alsinatriangular norm. KSII Trans. Internet Inf. Syst. 8(7), 2420–2433 (2014)

    Google Scholar 

  32. Ja’nos, A., Alsina, C.: Characterizations of some classes of quasilinear functions with applications to triangular norms and to synthesizing Judgements. Aequationes Math. 25(1), 313–315 (1982)

    Article  MathSciNet  Google Scholar 

  33. Mayer, N., Herrmann, J.M., Geisel, T.: Signatures of natural image statistics in cortical simple cell receptive fields. Neurocomputing 38, 279–284 (2001)

    Article  Google Scholar 

  34. Wang, N., Li, Q., El-Latif, A.A., peng, J., Niu, X.: Two-directional two-dimensional modified Fisher principal component analysis: an efficient approach for thermal face verification. J. Electron. Imaging 22(2), 023013 (2013)

    Article  Google Scholar 

  35. Wang, N., Li, Q., El-Latif, A.A., Peng, J., Niu, X.: An enhanced thermal face recognition method based on multiscale complex fusion for Gabor coefficients. Multimed. Tools Appl. 72(3), 2339–2358 (2014)

    Article  Google Scholar 

  36. Li, H., Sun, Z., Tan, T.: Robust iris segmentation based on learned boundary detectors. In: Proceedings of the Fifth APR International Conference on Biometrics, New Delhi, India, pp. 317–322 (2012)

  37. Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Chen, F., Wang, X.: A natural visible and infrared facial expression database for expression recognition and emotion inference. IEEE Trans. Multimed. 12(7), 682–691 (2010)

    Article  Google Scholar 

  38. Information technology-biometric performance testing and reporting, part 1: principles and framework. In: ISO/IEC 19795-1 (2006)

  39. Shen, W., Surette, M., Khanna, R.: Evaluation of automated biometrics-based identification and verification systems. Proc. IEEE 85(9), 1464–1478 (1997)

    Article  Google Scholar 

  40. Daugman, J.: Biometric decision landscapes. No. UCAM-CL-TR-482. Cambridge University, Computer Laboratory, (2000)

  41. He, M., Horng, S.-J., Fan, P., Run, R.-S., Chen, R.-J., Lai, J.-L., Khan, M.K., Sentosa, K.O.: Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recognit. 43(5), 1789–1800 (2010)

    Article  MATH  Google Scholar 

  42. Nandakumar, K., Chen, Y., Dass, S.C., Jain, A.K.: Likelihood ratio based biometric score fusion. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 342–347 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this paper through the Vice Deanship of Scientific Research Chairs.

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Authors and Affiliations

  1. Mathematics Department, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

    Ahmed A. Abd El-Latif

  2. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    M. Shamim Hossain

  3. Chair of Pervasive and Mobile Computing, King Saud University, Riyadh, Kingdom of Saudi Arabia

    M. Shamim Hossain

  4. Beijing Radio Measurement Technology Research Institute, Beijing, 100854, China

    Ning Wang

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  1. Ahmed A. Abd El-Latif
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  2. M. Shamim Hossain
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  3. Ning Wang
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Correspondence to M. Shamim Hossain.

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El-Latif, A.A.A., Hossain, M.S. & Wang, N. Score level multibiometrics fusion approach for healthcare. Cluster Comput 22 (Suppl 1), 2425–2436 (2019). https://doi.org/10.1007/s10586-017-1287-4

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  • DOI: https://doi.org/10.1007/s10586-017-1287-4

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