Development of an Automated Pain Facial Expression Detection System for Sheep (Ovis Aries).

The use of technology to optimize the production and management of each individual animal is becoming key to good farming. There is a need for the real-time systematic detection and control of disease in animals in order to limit the impact on animal welfare and food supply. Diseases such as footrot and mastitis cause significant pain in sheep, and so early detection is vital to ensuring effective treatment and preventing the spread across the flock. Facial expression scoring to assess pain in humans and non-humans is now well utilized, and the Sheep Pain Facial Expression Scale (SPFES) is a tool that can reliably detect pain in this species. The SPFES currently requires manual scoring, leaving it open to observer bias, and it is also time-consuming. The ability of a computer to automatically detect and direct a producer as to where assessment and treatment are needed would increase the chances of controlling the spread of disease. It would also aid in the prevention of resistance across the individual, farm, and landscape at both national and international levels. In this paper, we present our framework for an integrated novel system based on techniques originally applied for human facial expression recognition that could be implemented at the farm level. To the authors' knowledge, this is the first time that this technology has been applied to sheep to assess pain

Transferring Dense Pose to Proximal Animal Classes

Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail. In principle, the same approach could be extended to any animal class, but the effort required for collecting new annotations for each case makes this strategy impractical, despite important applications in natural conservation, science and business. We show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by (1) establishing a DensePose model for the new animal which is also geometrically aligned to humans (2) introducing a multi-head R-CNN architecture that facilitates transfer of multiple recognition tasks between classes, (3) finding which combination of known classes can be transferred most effectively to the new animal and (4) using self-calibrated uncertainty heads to generate pseudo-labels graded by quality for training a model for this class. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach, showing excellent transfer learning performance.Comment: Accepted at CVPR 2020; Project page: https://asanakoy.github.io/densepose-evolutio

Geometric morphometrics for the study of facial expressions in non-human animals, using the domestic cat as an exemplar

Facial expression is a common channel for the communication of emotion. However, in the case of non-human animals, the analytical methods used to quantify facial expressions can be subjective, relying heavily on extrapolation from human-based systems. Here, we demonstrate how geometric morphometrics can be applied in order to overcome these problems. We used this approach to identify and quantify changes in facial shape associated with pain in a non-human animal species. Our method accommodates individual variability, species-specific facial anatomy, and postural effects. Facial images were captured at four different time points during ovariohysterectomy of domestic short haired cats (n = 29), with time points corresponding to varying intensities of pain. Images were annotated using landmarks specifically chosen for their relationship with underlying musculature, and relevance to cat-specific facial action units. Landmark data were subjected to normalisation before Principal Components (PCs) were extracted to identify key sources of facial shape variation, relative to pain intensity. A significant relationship between PC scores and a well-validated composite measure of post-operative pain in cats (UNESP-Botucatu MCPS tool) was evident, demonstrating good convergent validity between our geometric face model, and other metrics of pain detection. This study lays the foundation for the automatic, objective detection of emotional expressions in a range of non-human animal species

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions