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Deep Face-Iris Recognition Using Robust Image Segmentation and Hyperparameter Tuning

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Computer Networks and Inventive Communication Technologies

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 75))

Abstract

Biometrics are increasingly being used for tasks that involve sensitive or financial data. Hitherto, security on devices such as smartphones has not been a priority. Furthermore, users tend to ignore the security features in favour of more rapid access to the device. A bimodal system is proposed that enhances security by utilizing face and iris biometrics from a single image. The motivation behind this is the ability to acquire both biometrics simultaneously in one shot. The system’s biometric components: face, iris(es) and their fusion are evaluated. They are also compared to related studies. The best results were yielded by a proposed lightweight Convolutional Neural Network architecture, outperforming tuned VGG-16, Xception, SVM and the related works. The system shows advancements to ‘at-a-distance’ biometric recognition for limited and high computational capacity computing devices. All deep learning algorithms are provided with augmented data, included in the tuning process, enabling additional accuracy gains. Highlights include near-perfect fivefold cross-validation accuracy on the IITD-Iris dataset when performing identification. Verification tests were carried out on the challenging CASIA-Iris-Distance dataset and performed well on few training samples. The proposed system is practical for small or large amounts of training data and shows great promise for at-a-distance recognition and biometric fusion.

Supported by the National Research Foundation (120654). This work was undertaken in the Distributed Multimedia CoE at Rhodes University.

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Notes

  1. 1.

    Memorizing and typing of secure passwords.

  2. 2.

    Random search algorithm of Keras-Tuner was used.

  3. 3.

    The smallest filter that captures left/right and up/down from a centre pixel’s perspective.

  4. 4.

    Very low probabilities \(\le \) 0.04 worked best.

  5. 5.

    1120 right-iris images from 224 subjects.

  6. 6.

    The second block preferring double the dropout rate was a consistent trend.

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Authors and Affiliations

  1. Rhodes University, 6140, Grahamstown, South Africa

    Dane Brown

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  1. Dane Brown
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Correspondence to Dane Brown .

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Editors and Affiliations

  1. Department of Information Technology, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Department of Telecommunication Engineering, Czech Technical University in Prague, Praha, Czech Republic

    Robert Bestak

  3. Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

  4. Faculty of Informatics and Information Technology, Slovak University Technology, Bratislava, Slovakia

    Ivan Kotuliak

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Brown, D. (2022). Deep Face-Iris Recognition Using Robust Image Segmentation and Hyperparameter Tuning. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_19

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  • DOI: https://doi.org/10.1007/978-981-16-3728-5_19

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  • Online ISBN: 978-981-16-3728-5

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