3D Face Recognition for Cows

Deepak Yeleshetty, Luuk Spreeuwers, Yan Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Downloads (Pure)

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%.

Original languageEnglish
Title of host publicationBIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group
EditorsArslan Bromme, Antitza Dantcheva, Christian Rathgeb, Christoph Busch, Kiran Raja, Andreas Uhl
Place of PublicationPiscataway, NJ
PublisherIEEE
ISBN (Electronic)978-3-88579-700-5
ISBN (Print)978-1-7281-8927-7
Publication statusPublished - Sep 2020
Event19th International Conference of the Biometrics Special Interest Group, BIOSIG 2020 - Darmstadt, Germany
Duration: 16 Sep 202018 Sep 2020

Publication series

NameProceedings of the International Conference of the Biometrics Special Interest Group, BIOSIG
PublisherIEEE
Volume2020
ISSN (Print)1617-5468

Conference

Conference19th International Conference of the Biometrics Special Interest Group, BIOSIG 2020
CountryGermany
CityDarmstadt
Period16/09/2018/09/20

Keywords

  • 3D face recognition
  • Biometrics
  • Cows
  • Iterative Closest Point
  • registration

Fingerprint Dive into the research topics of '3D Face Recognition for Cows'. Together they form a unique fingerprint.

Cite this