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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bhilare, Shruti | en_US |
dc.contributor.author | Kanhangad, Vivek | en_US |
dc.contributor.author | Chaudhari, Narendra S. | en_US |
dc.date.accessioned | 2022-03-17T01:00:00Z | - |
dc.date.accessioned | 2022-03-17T15:41:27Z | - |
dc.date.available | 2022-03-17T01:00:00Z | - |
dc.date.available | 2022-03-17T15:41:27Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Bhilare, 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.2017.7986767 | en_US |
dc.identifier.isbn | 9784901122160 | - |
dc.identifier.other | EID(2-s2.0-85027872586) | - |
dc.identifier.uri | https://doi.org/10.23919/MVA.2017.7986767 | - |
dc.identifier.uri | https://dspace.iiti.ac.in/handle/123456789/5300 | - |
dc.description.abstract | Biometric 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.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.source | Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 | en_US |
dc.subject | Biometrics | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Display devices | en_US |
dc.subject | Graphic methods | en_US |
dc.subject | Biometric recognition | en_US |
dc.subject | Histogram of oriented gradients | en_US |
dc.subject | Histogram of oriented gradients (HOG) | en_US |
dc.subject | Laplacian of gaussian filtering | en_US |
dc.subject | Local binary patterns | en_US |
dc.subject | Recognition systems | en_US |
dc.subject | Security solutions | en_US |
dc.subject | Smart-phone cameras | en_US |
dc.subject | Palmprint recognition | en_US |
dc.title | Histogram of oriented gradients based presentation attack detection in dorsal hand-vein biometric system | en_US |
dc.type | Conference Paper | en_US |
Appears in Collections: | Department of Electrical Engineering |
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