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MultiNet: A Multimodal Approach for Biometric Verification

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

Abstract

These days, the safety of personal information has become a matter of great concern for everyone. In this matter, the concept of Multimodal Biometrics has attracted the interest of the researchers because to the ability to solve a number of limitation of uni-modal biometric system. In this paper, we have presented multimodal biometric-based verification system, which is based on convolutional neural network to verify a individual using multi traits biometric modalities, i.e., fingerprint iris by score level fusion. We have achieved 98.8% accuracy over CASIA V fingerprint and iris dataset. Obtained results shows that using two different biometric trait in proposed biometric verification systems achieved better result than single biometric trait.

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Author information

Authors and Affiliations

  1. Centre for Advanced Studies, AKTU, Lucknow, India

    Poorti Sagar & Anamika Jain

  2. MIT-WPU, Pune, India

    Anamika Jain

Authors
  1. Poorti Sagar
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  2. Anamika Jain
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Corresponding author

Correspondence to Poorti Sagar .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Sagar, P., Jain, A. (2023). MultiNet: A Multimodal Approach for Biometric Verification. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_54

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