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Investigating of nodes and personal authentications utilizing multimodal biometrics for medical application of WBANs security

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Abstract

The authentication of the Wireless Body Area Networks (WBANs) nodes is a vital factor in its medical applications. This paper, investigates methods of authentication over these networks. Also, an effective unimodal and multimodal biometrics identification approaches based on individual face and voice recognition or combined using different fusion types are presented. The cryptography and non-cryptography-based authentication are discussed in this research work and its suitability with the medical applications. Cryptographic based authentication is not suitable for WBANs. The biometrics authentication is discussed and its challenges. In this work, different fusion types in multimodal biometric are presented. There are two unimodal schemes have been presented based on using the voice and face image individually, these two biometrics have been used in the multimodal biometric scheme. The presneted multimodal scheme is evaluated and applied using the feature and score fusion. The mechanism operation of presented algorithm starts with capturing the biometics signals ‘Face/Voice’, the second step is the feature extracting from each biometric individually. The Artificial Neural Network (ANN), The Support Vector Machine (SVM) and the Gaussian Mixture Model (GMM) classifiers have been employed to perform the classification process individually. The computer simulation experiments reveal that the cepstral coefficients and statistical coefficients for voice recognition performed better for the voice scenario. Also, the Eigenface and support vector machine tools in the face recognition scheme performed better than other schemes. The multimodal results better than the unimodal schemes. Also, the results of the scores fusion-based multimodal biometric scheme is better than the feature fusion-based scheme. Hence, the biometric-based authentication is effective and applicable for the WBANs authentication and personality continuous authentication on these medical applications wireless networks.

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Authors and Affiliations

  1. Electronics Technology Department, Faculty of Technology and Education, Helwan University, Cairo, Egypt

    Mohsen A. M. El-Bendary, Ayman Haggag & M. A. R. El-Tokhy

  2. Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, Egypt

    Hany Kasban

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  1. Mohsen A. M. El-Bendary
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  2. Hany Kasban
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  3. Ayman Haggag
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  4. M. A. R. El-Tokhy
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Correspondence to Mohsen A. M. El-Bendary.

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El-Bendary, M.A.M., Kasban, H., Haggag, A. et al. Investigating of nodes and personal authentications utilizing multimodal biometrics for medical application of WBANs security. Multimed Tools Appl 79, 24507–24535 (2020). https://doi.org/10.1007/s11042-020-08926-2

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