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Home / Equine Research Bank / Animals (Basel) / Towards Machine Recognition of Facial Expressions of Pain in...
Animals : an open access journal from MDPI2021; 11(6); 1643; doi: 10.3390/ani11061643

Towards Machine Recognition of Facial Expressions of Pain in Horses.

Abstract: 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.
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Publication Date: 2021-06-01 PubMed ID: 34206077PubMed Central: PMC8229776DOI: 10.3390/ani11061643Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research focuses on recognizing pain expressions in horses using machine learning principles and computer vision. The study devises tools for manual labeling, and explores the use of recurrent neural networks for a complete, end-to-end learning process.

Objective and Challenges

  • The research attempts to automate the recognition of facial expressions of pain in horses. Currently, automatic detection of human facial expressions is manageable due to advances in computer vision and machine learning, but this hasn’t been adequately applied to horses.
  • There are two main obstacles to this study: the lack of a large and annotated database for horse expressions, and difficulties in correctly identifying pain in horses given their inability to verbally communicate.

Pain Recognition Approach

  • The researchers employed two main methods to tackle the issue. The first one involved the use of the Facial Action Coding System (FACS), a manual yet relatively objective classification system for facial activities. After using this system to encode data, the researchers applied machine learning principles to analyze the data for pain expressions.
  • They created tools that assist with manual labeling. These tools can identify horse faces and facial features, providing a significant breakthrough towards the automatic recognition of facial action units from images.

Learning Process with Recurrent Neural Networks

  • The second method relied on recurrent neural network end-to-end learning, which requires less extraction of features and representations from videos. Instead, it depends heavily on a large amount of video data with ground truth.
  • The preliminary results of the study indicate that dynamics are crucial for recognizing pain. Furthermore, the use of recurrent neural networks for classification outperformed human raters when detecting experimental pain in horses.

Concluding Remarks

  • Though this study was preliminarily successful in automating the process of recognizing pain expressions in horses, it emphasizes the need for further research, specifically in compiling a comprehensive, annotated database of horse facial expressions.

Cite This Article

APA
Andersen PH, Broomé S, Rashid M, Lundblad J, Ask K, Li Z, Hernlund E, Rhodin M, Kjellström H. (2021). Towards Machine Recognition of Facial Expressions of Pain in Horses. Animals (Basel), 11(6), 1643. https://doi.org/10.3390/ani11061643

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 11
Issue: 6
PII: 1643

Researcher Affiliations

Andersen, Pia Haubro
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, SE 75007 Uppsala, Sweden.
Broomé, Sofia
  • Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, SE 100044 Stockholm, Sweden.
Rashid, Maheen
  • Department of Computer Science, University of California at Davis, California, CA 95616, USA.
Lundblad, Johan
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, SE 75007 Uppsala, Sweden.
Ask, Katrina
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, SE 75007 Uppsala, Sweden.
Li, Zhenghong
  • Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, SE 100044 Stockholm, Sweden.
  • Department of Computer Science, Stony Brook University, New York, NY 11794, USA.
Hernlund, Elin
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, SE 75007 Uppsala, Sweden.
Rhodin, Marie
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, SE 75007 Uppsala, Sweden.
Kjellström, Hedvig
  • Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, SE 100044 Stockholm, Sweden.

Grant Funding

  • 2020-01840 and 2016-01760 / Svenska Forskningsru00e5det Formas
  • 2016-03967 / Vetenskapsru00e5det

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

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