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Multi-resolution Local Descriptor for 3D Ear Recognition

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dc.contributor.authorGanapathi, Iyyakutti Iyappan
dc.contributor.authorAli, Syed Sadaf
dc.contributor.authorPrakash, Surya
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2020-09-15T13:01:28Z
dc.date.available2020-09-15T13:01:28Z
dc.date.issued2019
dc.description.abstractSeveral approaches have shown promising results in human ear recognition. However, factors such as the pose, illumination, and scaling have an enormous impact on recognition performance. 3D models are insensitive to these factors and are found to be better at enhancing recognition performance with strong geometric information. Low cost 3D data acquisition has also boosted the research community in recent times to explore more about 3D object recognition. We present a local multi-resolution descriptor in this paper to recognize human ears in 3D. For each key-point in 3D ear, a local reference frame (LRF) is constructed. Using multi-radii, we find neighbors at each key-point and the neighbors obtained from each radius are projected to create a depth image using the LRF. Further, a descriptor is computed by employing neural network based auto-encoders and the local statistics of the depth images. The descriptor is used to locate the potential correspondence matching points in the probe and gallery images for a coarse arrangement, followed by a fine alignment to compute the registration error. The obtained registration error is used as the matching score. The proposed technique is assessed on UND-J2 dataset to demonstrate its effectiveness.en
dc.identifier.isbn978-3-88579-690-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34234
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-297
dc.subject3D Biometrics
dc.subject3D Ear
dc.subjectHuman Recognition
dc.subjectAuthentication
dc.subjectSecurity
dc.subjectLocal Descriptor
dc.subjectHybrid Descriptor
dc.titleMulti-resolution Local Descriptor for 3D Ear Recognitionen
dc.typeText/Conference Paper
gi.citation.endPage228
gi.citation.publisherPlaceBonn
gi.citation.startPage221
gi.conference.date18.-20. September 2019
gi.conference.locationDarmstadt, Germany
gi.conference.sessiontitleFurther Conference Contributions

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P296 - BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group