Explainable Automated Recognition of Emotional States from Canine Facial Expressions: The Case of Positive Anticipation and Frustration
Bibliographic record
Abstract
Abstract Internal affective states are closely linked to facial expressions in both human and many non-human animals. For some animal species, objective tools for facial expression analysis such as AnimalFACS are available and are only just beginning to be increasingly used. However, their use requires special expertise, training, and certification, while there is still some remaining risk for human bias, and its application is time-consuming. Automation of facial analysis offers a promising alternative and is already being addressed by a large body of research in the human domain. In animal research, automation has so far been addressed for a few species in the context of pain, while emotional state recognition remains underexplored, especially in canine species due to the complexity of their facial morphology and expressions. The contribution of the present study is twofold. First, this is the first study to address automated recognition of emotional states in dogs 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). Two different approaches are compared in relation to our aim: (1) A DogFACS-based approach with a two-step pipeline consisting of (i) an Action Unit (AU) 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 the first to address the explainability of AI models in the context of understanding the expression 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 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 AUs. 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".