SwePub
Sök i LIBRIS databas

  Extended search

AMNE:(AGRICULTURAL SCIENCES) AMNE:(Veterinary Science) AMNE:(Medical Bioscience)
 

Search: AMNE:(AGRICULTURAL SCIENCES) AMNE:(Veterinary Science) AMNE:(Medical Bioscience) > Towards Machine Rec...

  • Haubro Andersen, PiaSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för kliniska vetenskaper (KV),Department of Clinical Sciences,Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, SE-75007 Uppsala, Sweden. (author)

Towards Machine Recognition of Facial Expressions of Pain in Horses

  • Article/chapterEnglish2021

Publisher, publication year, extent ...

  • 2021-06-01
  • MDPI,2021
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:kth-299017
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299017URI
  • https://doi.org/10.3390/ani11061643DOI
  • https://res.slu.se/id/publ/112767URI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:for swepub-publicationtype

Notes

  • QC 20210727
  • Simple Summary Facial activity can convey valid information about the experience of pain in a horse. However, scoring of pain in horses based on facial activity is still in its infancy and accurate scoring can only be performed by trained assessors. Pain in humans can now be recognized reliably from video footage of faces, using computer vision and machine learning. We examine the hurdles in applying these technologies to horses and suggest two general approaches to automatic horse pain recognition. The first approach involves automatically detecting objectively defined facial expression aspects that do not involve any human judgment of what the expression "means". Automated classification of pain expressions can then be done according to a rule-based system since the facial expression aspects are defined with this information in mind. The other involves training very flexible machine learning methods with raw videos of horses with known true pain status. The upside of this approach is that the system has access to all the information in the video without engineered intermediate methods that have filtered out most of the variation. However, a large challenge is that large datasets with reliable pain annotation are required. We have obtained promising results from both approaches. Automated recognition of human facial expressions of pain and emotions is to a certain degree a solved problem, using approaches based on computer vision and machine learning. However, the application of such methods to horses has proven difficult. Major barriers are the lack of sufficiently large, annotated databases for horses and difficulties in obtaining correct classifications of pain because horses are non-verbal. This review describes our work to overcome these barriers, using two different approaches. One involves the use of a manual, but relatively objective, classification system for facial activity (Facial Action Coding System), where data are analyzed for pain expressions after coding using machine learning principles. We have devised tools that can aid manual labeling by identifying the faces and facial keypoints of horses. This approach provides promising results in the automated recognition of facial action units from images. The second approach, recurrent neural network end-to-end learning, requires less extraction of features and representations from the video but instead depends on large volumes of video data with ground truth. Our preliminary results suggest clearly that dynamics are important for pain recognition and show that combinations of recurrent neural networks can classify experimental pain in a small number of horses better than human raters.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Broomé, SofiaKTH,Robotik, perception och lärande, RPL(Swepub:kth)u1oh25at (author)
  • Rashid, MaheenUniv Calif Davis, Dept Comp Sci, Davis, CA 95616 USA. (author)
  • Lundblad, JohanSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för anatomi, fysiologi och biokemi,Department of Anatomy, Physiology and Biochemistry (AFB),Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, SE-75007 Uppsala, Sweden. (author)
  • Ask, KatrinaSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för anatomi, fysiologi och biokemi,Department of Anatomy, Physiology and Biochemistry (AFB),Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, SE-75007 Uppsala, Sweden.(Swepub:slu)100372 (author)
  • Li, ZhenghongKTH,Robotik, perception och lärande, RPL,SUNY Stony Brook, Dept Comp Sci, Stony Brook, NY 11794 USA.(Swepub:kth)u1l2ai02 (author)
  • Hernlund, ElinSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för anatomi, fysiologi och biokemi,Department of Anatomy, Physiology and Biochemistry (AFB),Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, SE-75007 Uppsala, Sweden.(Swepub:slu)48530 (author)
  • Rhodin, MarieSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för anatomi, fysiologi och biokemi,Department of Anatomy, Physiology and Biochemistry (AFB),Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, SE-75007 Uppsala, Sweden.(Swepub:slu)47433 (author)
  • Kjellström, Hedvig,1973-KTH,Robotik, perception och lärande, RPL(Swepub:kth)u1izkbhh (author)
  • Sveriges lantbruksuniversitetInstitutionen för kliniska vetenskaper (KV) (creator_code:org_t)
  • Sveriges lantbruksuniversitet

Related titles

  • In:Animals: MDPI11:62076-2615

Internet link

Find in a library

  • Animals (Search for host publication in LIBRIS)

To the university's database

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view