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Towards More Accurate Touchless Fingerprint Classification Using Deep Learning and SVM

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Data Science and Computational Intelligence (ICInPro 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1483))

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Abstract

Automated fingerprint classification is one of the mostly used human identity verification system. The touchless fingerprint identification and classification system offers a higher user convenience and hygiene compared to conventional touch-based identification. Recently, convolutional neural networks (CNN) are trained to achieve better performance on fingerprint classification. For classification, deep learning models utilizes the SoftMax layer for prediction which limits cross entropy loss. In this proposed method SoftMax layer is replaced by multi class Support Vector Machine which reduces the margin-based loss as well. There are various combinations of deep learning models and support vector machines. In this paper experiments on AlexNet with multi class SVM and AlexNet with SoftMax have been demonstrated on PolyU 3D benchmark fingerprint Database to improve the recognition accuracy on test data set and to minimize the training time involved. The results show that the deep learning model with SVM achieve better results both in terms of validation accuracy and time in training than the modified transfer learning network with SoftMax as classifier.

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Acknowledgement

The Authors want to thank the principal, authorities and administration of Malnad College of Engineering, Hassan for broadening full help in carrying this research work.

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Authors and Affiliations

  1. Department of E&C, MCE, VTU, Hassan, Belagavi, India

    K. C. Deepika

  2. Department of E&I, MCE, VTU, Hassan, Belagavi, India

    G. Shivakumar

Authors
  1. K. C. Deepika
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  2. G. Shivakumar
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Corresponding author

Correspondence to K. C. Deepika .

Editor information

Editors and Affiliations

  1. Bangalore University, Bengaluru, India

    K. R. Venugopal

  2. Bangalore University, Bengaluru, India

    P. Deepa Shenoy

  3. University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. NIAS, Bengaluru, India

    L. M. Patnaik

  5. Florida International University, Miami, FL, USA

    Sitharama S. Iyengar

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Conflict of Interest on behalf of both the authors, the corresponding author states that there is no conflict of interest.

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Deepika, K.C., Shivakumar, G. (2021). Towards More Accurate Touchless Fingerprint Classification Using Deep Learning and SVM. In: Venugopal, K.R., Shenoy, P.D., Buyya, R., Patnaik, L.M., Iyengar, S.S. (eds) Data Science and Computational Intelligence. ICInPro 2021. Communications in Computer and Information Science, vol 1483. Springer, Cham. https://doi.org/10.1007/978-3-030-91244-4_20

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  • DOI: https://doi.org/10.1007/978-3-030-91244-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91243-7

  • Online ISBN: 978-3-030-91244-4

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