ArticleScientific reports2022
Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Non-invasive canine electroencephalography (EEG): a systematic review.BMC veterinary research · 2025Pooled it
- Artificial Intelligence-Based Decoding of Animal Micro-Expressions: A Review of Methodological Advances and Translational Applications.Animals : an open access journal from MDPI · 2026Review
- Comparing the performance of deep learning video-based models and trained veterinarians in cattle pain assessment.Scientific reports · 2026Article
- Research on improved models for facial expression recognition in mice with abnormal glucose metabolism.Scientific reports · 2026Article
- Investigating the capabilities of large vision language models in dog emotion recognition.Scientific reports · 2025Article
- What Does That Head Tilt Mean? Brain Lateralization and Sex Differences in the Processing of Familiar Human Speech by Domestic Dogs.Animals : an open access journal from MDPI · 2025Article
- Research Methods for the Analysis of Visual Emotion Cues in Animals: A Workshop Report.Animals : an open access journal from MDPI · 2025Article
- Automated analysis of emotional expressions in dogs based on geometric morphometrics.Scientific reports · 2025Article
- Differences in dogs' and wolves' human-directed greeting behaviour: facial expressions, body language, and the problem of human biases.Animal cognition · 2025Article
- Dog facial landmarks detection and its applications for facial analysis.Scientific reports · 2025Article
- Continuous automated analysis of facial dynamics of brachycephalic and normocephalic dogs in different contexts.BMC veterinary research · 2025Article
- A segment-based framework for explainability in animal affective computing.Scientific reports · 2025Article
- Facial expressions during compound interventions of nociception, conspecific isolation, and sedation in horses.Scientific reports · 2025Article
- Generative AI Meets Animal Welfare: Evaluating GPT-4 for Pet Emotion Detection.Animals : an open access journal from MDPI · 2025Article
- Automated video-based pain recognition in cats using facial landmarks.Scientific reports · 2024Article
- Computational investigation of the social function of domestic cat facial signals.Scientific reports · 2024Article
- Coyotes can do 'puppy dog eyes' too: comparing interspecific variation inRoyal Society open science · 2024Article
- Automation in canine science: enhancing human capabilities and overcoming adoption barriers.Frontiers in veterinary science · 2024Article
- Automated landmark-based cat facial analysis and its applications.Frontiers in veterinary science · 2024Article
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Corrections and comments
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs' facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network's attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye.
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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.