3D Face Recognition For Cows
dc.contributor.author | Yeleshetty, Deepak | |
dc.contributor.author | Spreeuwers, Luuk | |
dc.contributor.author | Li, Yan | |
dc.contributor.editor | Brömme, Arslan | |
dc.contributor.editor | Busch, Christoph | |
dc.contributor.editor | Dantcheva, Antitza | |
dc.contributor.editor | Raja, Kiran | |
dc.contributor.editor | Rathgeb, Christian | |
dc.contributor.editor | Uhl, Andreas | |
dc.date.accessioned | 2020-09-16T08:25:44Z | |
dc.date.available | 2020-09-16T08:25:44Z | |
dc.date.issued | 2020 | |
dc.description.abstract | This paper presents a method to recognize cows using their 3D face point clouds. Face is chosen because of the rigid structure of the skull compared to other parts. The 3D face point clouds are acquired using a newly designed dual 3D camera setup. After registering the 3D faces to a specific pose, the cow’s ID is determined by running Iterative Closest Point (ICP) method on the probe against all the point clouds in the gallery. The root mean square error (RMSE) between the ICP correspondences is used to identify the cows. The smaller the RMSE, the more likely that the cow is from the same class. In a closed set of 32 cows with 5 point clouds per cow in the gallery, the ICP recognition demonstrates an almost perfect identification rate of 99.53%. | en |
dc.identifier.isbn | 978-3-88579-700-5 | |
dc.identifier.pissn | 1617-5468 | |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/34323 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik e.V. | |
dc.relation.ispartof | BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-306 | |
dc.subject | Cows | |
dc.subject | Biometrics | |
dc.subject | Visual identification | |
dc.subject | 3D face recognition | |
dc.subject | Pointcloud registration | |
dc.subject | Iterative Closest Point | |
dc.subject | Realsense cameras. | |
dc.title | 3D Face Recognition For Cows | en |
dc.type | Text/Conference Paper | |
gi.citation.endPage | 171 | |
gi.citation.publisherPlace | Bonn | |
gi.citation.startPage | 163 | |
gi.conference.date | 16.-18. September 2020 | |
gi.conference.location | International Digital Conference | |
gi.conference.sessiontitle | Regular Research Papers |
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