ArticleFrontiers in veterinary science2024
Automated landmark-based cat facial analysis and its applications.
Article in Frontiers in veterinary science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Artificial Intelligence-Based Decoding of Animal Micro-Expressions: A Review of Methodological Advances and Translational Applications.Animals : an open access journal from MDPI · 2026Review
- Automated analysis of feline posture using deep learning and geometric modeling.Frontiers in veterinary science · 2026Article
- Automated analysis of emotional expressions in dogs based on geometric morphometrics.Scientific reports · 2025Article
- Dog facial landmarks detection and its applications for facial analysis.Scientific reports · 2025Article
- Effects of feline personality on pain assessment with two validated behaviour-based scales.Journal of feline medicine and surgery · 2025Article
- Continuous automated analysis of facial dynamics of brachycephalic and normocephalic dogs in different contexts.BMC veterinary research · 2025Article
- Clinical interpretation of body language and behavioral modifications to recognize pain in domestic mammals.Frontiers in veterinary science · 2025Review
- Faces of time: a historical overview of rapid innovations in coding animal facial signals.Frontiers in veterinary science · 2025Review
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Authors and funding
10 authors.
Funding
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Abstract
Facial landmarks, widely studied in human affective computing, are beginning to gain interest in the animal domain. Specifically, landmark-based geometric morphometric methods have been used to objectively assess facial expressions in cats, focusing on pain recognition and the impact of breed-specific morphology on facial signaling. These methods employed a 48-landmark scheme grounded in cat facial anatomy. Manually annotating these landmarks, however, is a labor-intensive process, deeming it impractical for generating sufficiently large amounts of data for machine learning purposes and for use in applied real-time contexts with cats. Our previous work introduced an AI pipeline for automated landmark detection, which showed good performance in standard machine learning metrics. Nonetheless, the effectiveness of fully automated, end-to-end landmark-based systems for practical cat facial analysis tasks remained underexplored. In this paper we develop AI pipelines for three benchmark tasks using two previously collected datasets of cat faces. The tasks include automated cat breed recognition, cephalic type recognition and pain recognition. Our fully automated end-to-end pipelines reached accuracy of 75% and 66% in cephalic type and pain recognition respectively, suggesting that landmark-based approaches hold promise for automated pain assessment and morphological explorations.
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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.