Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/5300
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dc.contributor.authorBhilare, Shrutien_US
dc.contributor.authorKanhangad, Viveken_US
dc.contributor.authorChaudhari, Narendra S.en_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-17T15:41:27Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-17T15:41:27Z-
dc.date.issued2017-
dc.identifier.citationBhilare, S., Kanhangad, V., & Chaudhari, N. (2017). Histogram of oriented gradients based presentation attack detection in dorsal hand-vein biometric system. Paper presented at the Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017, 39-42. doi:10.23919/MVA.2017.7986767en_US
dc.identifier.isbn9784901122160-
dc.identifier.otherEID(2-s2.0-85027872586)-
dc.identifier.urihttps://doi.org/10.23919/MVA.2017.7986767-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/5300-
dc.description.abstractBiometric recognition, which is an integral part of the present-day security solutions, faces a major threat from presentation or spoofing attacks. In this paper, we present a novel presentation attack detection (PAD) approach for dorsal hand-vein based recognition system. The proposed approach performs Laplacian of Gaussian filtering on the acquired images, followed by extraction of histogram of oriented gradients (HOG) features at multiple scales. A linear SVM is employed for each scale and the final decision is obtained by combining individual decisions using the majority voting scheme. Experiments were carried out on 624 real images and 624 artefacts (spoof samples) collected from left and right hands of 52 subjects. Artefacts were generated independent of the enrollment images, by employing an off-the-shelf smartphone camera to capture the vein patterns from users' hands. These images were displayed using two different display devices and presented as artefacts to the biometric sensor. The experiments were carried out in the same-device and the cross-device scenarios. Our approach achieves average error rate of 0.16% and 0.8% in the same-device and the cross-device experiments, respectively and outperforms local binary patterns (LBP) based baseline algorithm. © 2017 MVA Organization All Rights Reserved.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017en_US
dc.subjectBiometricsen_US
dc.subjectComputer visionen_US
dc.subjectDisplay devicesen_US
dc.subjectGraphic methodsen_US
dc.subjectBiometric recognitionen_US
dc.subjectHistogram of oriented gradientsen_US
dc.subjectHistogram of oriented gradients (HOG)en_US
dc.subjectLaplacian of gaussian filteringen_US
dc.subjectLocal binary patternsen_US
dc.subjectRecognition systemsen_US
dc.subjectSecurity solutionsen_US
dc.subjectSmart-phone camerasen_US
dc.subjectPalmprint recognitionen_US
dc.titleHistogram of oriented gradients based presentation attack detection in dorsal hand-vein biometric systemen_US
dc.typeConference Paperen_US
Appears in Collections:Department of Electrical Engineering

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