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dc.contributor.authorMohaghegh, Men_NZ
dc.contributor.authorPayne, Aen_NZ
dc.date.accessioned2021-11-10T03:27:06Z
dc.date.available2021-11-10T03:27:06Z
dc.date.copyright2021en_NZ
dc.identifier.citationJournal of Physics: Conference Series. 1880 012014
dc.identifier.issn1742-6588en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/14650
dc.description.abstractThe identification of perpetrators, present in Child Sexual Abuse Imagery (CSAI), is a significant challenge due to the use of anonymisation techniques that mask their identities. Consequently, researchers have investigated the use of uncommon biometric identifiers such as knuckle patterns, palmprints and the dorsal side of the hand. This research proposes a Convolutional Neural Network (CNN) based, fully automated approach to biometric identification using dorsal hand images. The identification performance of three different CNN architectures, AlexNet, ResNet50 and ResNet152, is experimentally determined against two similar datasets, the 11k Hands and IITD dorsal hand databases. A transfer learning approach is used and the final output layers of the CNNs are modified to match the number of classes present in the datasets. The results showed that ResNet CNNs achieved identification accuracies greater than 99.9% on both datasets, whereas the AlexNet CNN achieved between 80.1% and 93.7%. These results demonstrate that it is feasible to use deep, off-the-shelf CNNs, such as ResNets, for automated biometric identification using dorsal hand images. This highlights the potential of using dorsal hand images to identify perpetrators of child sexual abuse from CSAI.
dc.publisherIOP Publishing
dc.relation.urihttps://iopscience.iop.org/article/10.1088/1742-6596/1880/1/012014en_NZ
dc.rightsContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd.
dc.titleAutomated Biometric Recognition Using Dorsal Hand Images and Convolutional Neural Networksen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1088/1742-6596/1880/1/012014en_NZ
aut.relation.volume1880en_NZ
pubs.elements-id395849
aut.relation.conference5th International Conference on Machine Vision and Information Technology (CMVIT 2021)en_NZ


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