A Multimodal Authentication for Biometric Recognition System using Intelligent Hybrid Fusion Techniques.

Biometric Recognition and Authentication is used in many applications for the secured identification of the persons. Several Researches has been carried out to strengthen the security algorithms through which the identification can be done in secured manner. With this objective, a new algorithm call...

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Publicado en:Journal of Medical Systems Vol. 43; no. 8
Autores principales: Prabu, S., Lakshmanan, M., Mohammed, V. Noor
Formato: equations & formulas pictorial research tables/charts Journal Article
Publicado: Springer Nature Aug2019
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Aug2019
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1391-5
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        atl: A Multimodal Authentication for Biometric Recognition System using Intelligent Hybrid Fusion Techniques.
      aug:
        au:
          Prabu, S.
          Lakshmanan, M.
          Mohammed, V. Noor
        affil: Department of ECE, Mahendra Institute of Technology, Namakkal, Tamilnadu, India
      sug:
        subj:
          Biometrics Methods
          Algorithms
          Hand
          Iris
          Machine Learning
          Systems Development
          Descriptive Statistics
      ab: Biometric Recognition and Authentication is used in many applications for the secured identification of the persons. Several Researches has been carried out to strengthen the security algorithms through which the identification can be done in secured manner. With this objective, a new algorithm called Hybrid Adaptive Fusion(HAF) has been proposed which works on the principle of hybrid fusion of two feature inputs such as Hand geometry and iris of the users. As mentioned, the proposed algorithm uses the novel and hybrid fusion of feature extraction along with the accurate machine learning classifier. Effective Linear Binary Patterns (ELBP) and Scale Invariant Fourier Transform (SIFT) are stored in the databases for the further verification. The features stored are fed into the Extreme Learning machines for the detection of the verified users. This algorithm has been tested with the CASIA Image Datasets and with the different classifiers such as Neural Networks, Baiyes Networks. The proposed algorithm with ELM has better accuracy of 98.5% when compared with the other machine learning algorithms.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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