Image Compression using Discrete Cosine Transform (DCT) and Features Level Fusion in the Recognition for Multimodal Authentication Biometrics System

  • Authors

    • Fatma Susilawati Mohamad
    • Khaled Alhadi Meftah
    https://doi.org/10.14419/ijet.v7i3.28.23414
  • Multimodal Biometrics, Fusion, CCA, DCT, Features Level Fusion.

  • Abstract

    Multimodal biometrics have an important role in security systems by detecting security breaches and authentication systems, as well as security and confidentiality of information transmission. Sometimes, some factors affect the system's authentication noise and lightness when using a single biometric. So, in this paper, we will present a proposal for an authentication system through the compression dataset images using Discrete Cosine Transform (DCT). After extracting the features of each biometric separately (such as face - fingerprint - fingervein - iris), features extraction were normalized and all two biometrics were fusion (such as Face & Fingerprint – Face & Fingervein - Face & Iris – Fingerprint & Fingervein – Fingerprint & Iris – Fingervein & Iris) by the application of a method- Canonical Correlation Analysis (CCA). Recognition results were recorded. We obtained the best recognition rate between each merger by combining biometrics and we find the best rate of accuracy 98.1132%.

     

  • References

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  • How to Cite

    Susilawati Mohamad, F., & Alhadi Meftah, K. (2018). Image Compression using Discrete Cosine Transform (DCT) and Features Level Fusion in the Recognition for Multimodal Authentication Biometrics System. International Journal of Engineering & Technology, 7(3.28), 173-176. https://doi.org/10.14419/ijet.v7i3.28.23414

    Received date: 2018-12-08

    Accepted date: 2018-12-08