ReviewAnimals : an open access journal from MDPI2021
Towards Machine Recognition of Facial Expressions of Pain in Horses.
Review in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
30 citing papers in PubMed, 65 citations in OpenAlex.
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- Smartphone-Based Technologies in Equine Sports Medicine: Supporting Athlete Management-A Review.Sensors (Basel, Switzerland) · 2026Review
- Detecting Dairy Cattle Protective Behaviors via a Multi-Stage Attention SlowFast Network.Animals : an open access journal from MDPI · 2026Article
- Research on improved models for facial expression recognition in mice with abnormal glucose metabolism.Scientific reports · 2026Article
- Research Methods for the Analysis of Visual Emotion Cues in Animals: A Workshop Report.Animals : an open access journal from MDPI · 2025Article
- Using an Equine Cadaver Head to Investigate Associations Between Sub-Noseband Space, Noseband Tension, and Sub-Noseband Pressure at Three Locations.Animals : an open access journal from MDPI · 2025Article
- An iterative approach to identify key predictive features of fear reactivity and fearfulness in horses (Equus caballus).Scientific reports · 2025Article
- Continuous automated analysis of facial dynamics of brachycephalic and normocephalic dogs in different contexts.BMC veterinary research · 2025Article
- Using deep learning models to decode emotional states in horses.Scientific reports · 2025Article
- Identifying Novel Emotions and Wellbeing of Horses from Videos Through Unsupervised Learning.Sensors (Basel, Switzerland) · 2025Article
- Digitalising behavioural data collection through cloud-based technology in veterinary science and beyond.Frontiers in veterinary science · 2025Article
- Faces of time: a historical overview of rapid innovations in coding animal facial signals.Frontiers in veterinary science · 2025Review
- Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings.Scientific reports · 2024Article
- Changes in the equine facial repertoire during different orthopedic pain intensities.Scientific reports · 2024Article
- From facial expressions to algorithms: a narrative review of animal pain recognition technologies.Frontiers in veterinary science · 2024Review
- Poor Association between Facial Expression and Mild Lameness in Thoroughbred Trot-Up Examinations.Animals : an open access journal from MDPI · 2023Article
- Analysis of Various Facial Expressions of Horses as a Welfare Indicator Using Deep Learning.Veterinary sciences · 2023Article
- Development, refinement, and validation of an equine musculoskeletal pain scale.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
- Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.