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IoT-based multimodal liveness detection using the fusion of ECG and fingerprint

IoT-based multimodal liveness detection using the fusion of ECG and fingerprint Biometric scans using fingerprints are widely used for security purposes. Eventually, for authentication purposes, fingerprint scans are not very reliable because they can be faked by obtaining a sample of the fingerprint of the person. There are a few spoof detection techniques available to reduce the incidence of spoofing of the biometric system. Among them, the most commonly used is the binary classification technique that detects real or fake fingerprints based on the fingerprint samples provided during training. However, this technique fails when it is provided with samples formed using other spoofing techniques that are different from the spoofing techniques covered in the training samples. This paper aims to improve the liveness detection accuracy by fusing electrocardiogram (ECG) and fingerprint.Design/methodology/approachIn this paper, to avoid this limitation, an efficient liveness detection algorithm is developed using the fusion of ECG signals captured from the fingertips and fingerprint data in Internet of Things (IoT) environment. The ECG signal will ensure the detection of real fingerprint samples from fake ones.FindingsSingle model fingerprint methods have some disadvantages, such as noisy data and position of the fingerprint. To overcome this, fusion of both ECG and fingerprint is done so that the combined data improves the detection accuracy.Originality/valueSystem security is improved in this approach, and the fingerprint recognition rate is also improved. IoT-based approach is used in this work to reduce the computation burden of data processing systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Pervasive Computing and Communications Emerald Publishing

IoT-based multimodal liveness detection using the fusion of ECG and fingerprint

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References (22)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1742-7371
eISSN
1742-7371
DOI
10.1108/ijpcc-10-2021-0248
Publisher site
See Article on Publisher Site

Abstract

Biometric scans using fingerprints are widely used for security purposes. Eventually, for authentication purposes, fingerprint scans are not very reliable because they can be faked by obtaining a sample of the fingerprint of the person. There are a few spoof detection techniques available to reduce the incidence of spoofing of the biometric system. Among them, the most commonly used is the binary classification technique that detects real or fake fingerprints based on the fingerprint samples provided during training. However, this technique fails when it is provided with samples formed using other spoofing techniques that are different from the spoofing techniques covered in the training samples. This paper aims to improve the liveness detection accuracy by fusing electrocardiogram (ECG) and fingerprint.Design/methodology/approachIn this paper, to avoid this limitation, an efficient liveness detection algorithm is developed using the fusion of ECG signals captured from the fingertips and fingerprint data in Internet of Things (IoT) environment. The ECG signal will ensure the detection of real fingerprint samples from fake ones.FindingsSingle model fingerprint methods have some disadvantages, such as noisy data and position of the fingerprint. To overcome this, fusion of both ECG and fingerprint is done so that the combined data improves the detection accuracy.Originality/valueSystem security is improved in this approach, and the fingerprint recognition rate is also improved. IoT-based approach is used in this work to reduce the computation burden of data processing systems.

Journal

International Journal of Pervasive Computing and Communications – Emerald Publishing

Published: Nov 8, 2024

Keywords: Binary classification; Fingerprint; Image fusion; Electrocardiogram (ECG); Biometric scan; Liveness detection

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