Features Level Fusion through Multimodal Biometrics by using Face, Finger Vein, Fingerprint and Iris

  • Authors

    • Fatma Susilawati Mohamad
    • Khaled Alhadi Meftah
    https://doi.org/10.14419/ijet.v7i3.28.23413
  • Multimodal Biometrics, Fusion, Features Extraction, Accuracy Rate.

  • Abstract

    The fusion of the extracted attributes of the individual two biometrics (such as Face & Fingerprint – Face & Iris – Fingervein and Fingerprint …..) were analyzed. The effect of the Discrete Cosine Transform (DCT) were obtained for compression dataset images and normalize attributes. This fusion increases the strength of system security and authentication, noise when using authentication devices. The authentication procedure plays a pivotal role in the process of security and confidentiality of information as it involves in recognising the users of the device and it might be the failure of the devices to recognise the person via a single dynamic measurement such as fingerprint only become what might be altered by some noise or the effect of lighting. This paper will be dealt with the fusion of four attributes extraction of all the existing biometrics with minimum, maximum, summation, average, and standard deviation for every attributes and fusion them in a new matrix and the similar procedures used before (such as Fingerprint & Face & Fingervein & Iris). After executing this proposal, the best rate of recognition is (100%) when used fusion among the attributes for four biometric including the best accuracy rate (98.9247%).

     


     

  • References

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  • How to Cite

    Susilawati Mohamad, F., & Alhadi Meftah, K. (2018). Features Level Fusion through Multimodal Biometrics by using Face, Finger Vein, Fingerprint and Iris. International Journal of Engineering & Technology, 7(3.28), 170-172. https://doi.org/10.14419/ijet.v7i3.28.23413

    Received date: 2018-12-08

    Accepted date: 2018-12-08