The field of animal affective computing is rapidly emerging, and analysis of facial expressions is a crucial aspect. One of the most significant challenges that researchers in the field currently face is the scarcity of high-quality, comprehensive datasets that allow the development of models for facial expressions analysis. One of the possible approaches is the utilisation of facial landmarks, which has been shown for humans and animals. In this paper we present a novel dataset of cat facial images annotated with bounding boxes and 48 facial landmarks grounded in cat facial anatomy. We also introduce a landmark detection convolution neural network-based model which uses a magnifying ensemble method. Our model shows excellent performance on cat faces and is generalizable to human and other animals facial landmark detection.

This study develops a novel automated method for measuring continuous dynamics of dog facial behavior based on video-based tracking of 46 facial landmarks grounded in the Dog Facial Action Coding System. This method is applied for comparing the facial behavior of ( $$n_1=7$$ ) brachycephalic (Boston Terrier) and ( $$n_2=7$$ ) normocephalic (Jack Russel Terrier) dogs in four different contexts, eliciting various inner states: positive (play and called by name) and negative (separation and stranger). Having objectively quantified facial dynamics in brachycephalic and normocephalic dogs, we have found that brachycephalic dogs exhibited consistently lower facial dynamics across all four tested contexts and facial regions (eyes, mouth, and ears) compared to normocephalic dogs. They further demonstrated relatively higher dynamics in positive play and negative stranger conditions than in the other two conditions. In contrast, normocephalic dogs showed elevated dynamics exclusively in the positive play condition, with significantly reduced dynamics in the negative stranger condition. These findings highlight distinct patterns of facial expressivity between the two morphological groups, suggesting decreased facial expression in brachycephalic dogs and demonstrating our method’s value in providing novel insights into canine communication.

Automated analysis of facial expressions is a crucial challenge in the emerging field of animal affective computing. One of the most promising approaches in this context is facial landmarks, which are well-studied for humans and are now being adopted for many non-human species. The scarcity of high-quality, comprehensive datasets is a significant challenge in the field. This paper is the first to present a novel Dog Facial Landmarks in the Wild (DogFLW) dataset containing 3732 images of dogs annotated with facial landmarks and bounding boxes. Our facial landmark scheme has 46 landmarks grounded in canine facial anatomy, the Dog Facial Action Coding System (DogFACS), and informed by existing cross-species landmarking methods. We additionally provide a benchmark for dog facial landmarks detection and demonstrate two case studies for landmark detection models trained on the DogFLW. The first is a pipeline using landmarks for emotion classification from dog facial expressions from video, and the second is the recognition of DogFACS facial action units (variables), which can enhance the DogFACS coding process by reducing the time needed for manual annotation. The DogFLW dataset aims to advance the field of animal affective computing by facilitating the development of more accurate, interpretable, and scalable tools for analysing facial expressions in dogs with broader potential applications in behavioural science, veterinary practice, and animal-human interaction research.

Detection dogs are utilized for searching and alerting to various substances due to their olfactory abilities. Dog trainers report being able to ‘predict’ such identification based on subtle behavioural changes, such as tail movement. This study investigated tail kinematic patterns of dogs during a detection task, using computer vision to detect tail movement. Eight dogs searched for a target odour on a search wall, alerting to its presence by standing still. Dogs’ detection accuracy against a distractor odour was 100% with trained concentration, while during threshold assessment, it progressively reached 50%. In the target odour area, dogs exhibited a higher left-sided tail-wagging amplitude. An artificial intelligence (AI) model showed a 77% accuracy score in the classification, and, in line with the dogs’ performance, progressively decreased at lower odour concentrations. Additionally, we compared the performance of an AI classification model to that of 190 detection dog handlers in determining when a dog was in the vicinity of a target odour. The AI model outperformed dog professionals, correctly classifying 66% against 46% of videos. These findings indicate the potential of AI-enhanced techniques to reveal new insights into dogs’ behavioural repertoire during odour discrimination.

Automated analysis of facial expressions is a vibrant field in human affective computing, while research in nonhuman animals is still in its early stages. Compared to labor-intensive manual coding, automation can provide a more reliable and objective alternative, eliminating subjectivity and bias. However, using automated approaches of facial analysis in nonhuman animals ''in the wild”, i.e. outside of controlled laboratory conditions, is a challenge given the nature of noisy datasets. Here we present the first study using a fully automated analysis of facial landmarks associated with different emotional states in a morphologically diverse sample of pet dogs. We applied a novel AI-pipeline to study fear expressions of dogs in their home environment, analysing owner-provided video recordings during a real-life firework situation on New Year’s Eve in comparison to a control evening without fireworks. Using a static geometric morphometrics-inspired analysis, the pipeline allows for quantifying dog facial expressions in an extremely noisy and diverse ''in the wild” dataset, encompassing various breeds, angles and environments. We used an automated facial landmark system of 46 dog facial landmarks based on the Dog Facial Action Coding System. Due to the great variety in morphology of the included dogs, landmarks denoting the ear pinnae were excluded. Nonetheless, landmarks relating to the base of the ears differentiated most strongly between the conditions, suggesting backwards-drawn ears as the best indicator of the firework condition, which is in agreement with manually coded data. Additionally, the firework condition was associated with more mouth-opening, possibly reflecting panting in a subset of dogs. We conclude that automated analysis of dog facial expressions, based on the previously validated landmark system, is feasible in a diverse sample of pet dogs, paving the way towards automated emotion detection.