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Benefits of Gaussian Convolution in Gait Recognition

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dc.contributor.authorMarsico, Maria De
dc.contributor.authorMecca, Alessio
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:24Z
dc.date.available2019-06-17T10:00:24Z
dc.date.issued2018
dc.description.abstractThe first and still popular approach to gait recognition applies computer vision techniques to appearance-based features of walking patterns. More recently, wearable sensors have become attractive. The accelerometer is the most used one, being embedded in widespread mobile devices. Related techniques do not suffer for problems like occlusion and point of view, but for intra-subject variations caused by walking speed, ground type, shoes, etc. However, we can often recognize a person from the walking pattern, and this stimulates to search for robust features, able to sufficiently characterize this trait. This paper presents some preliminary experiments using the convolution with Gaussian kernels to extract relevant gait elements. The experiments use the large ZJU-gaitacc public dataset, and achieve improved results compared with previous works exploiting the same dataset.en
dc.identifier.isbn978-3-88579-676-4
dc.identifier.pissn1617-5469
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23799
dc.language.isoen
dc.publisherKöllen Druck+Verlag GmbH
dc.relation.ispartofBIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-283
dc.subjectGait Recognition
dc.subjectBiometrics
dc.subjectGaussian Kernel
dc.titleBenefits of Gaussian Convolution in Gait Recognitionen
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.conference.date26.-28. September 2018
gi.conference.locationDarmstadt

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P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group