Featured researcher: Professor Melvyn Smith
State-of-the-art facial recognition technology is being used in an attempt to detect different emotional states in pigs.
Machine vision experts at the University of the West of England (UWE Bristol) have teamed up with animal behaviourists from Scotland’s Rural College (SRUC) in Edinburgh for the study, which it is hoped will lead to a tool that can monitor individual animals’ faces and alert farmers to any health and welfare problems.
Pigs are highly expressive and SRUC research has previously shown they can signal their intentions to other pigs using different facial expressions. There is also evidence of different expressions when they are in pain or under stress.
At SRUC’s Pig Research Centre in Midlothian, scientists are capturing 3D and 2D facial images of the breeding sow population under various, typical commercial situations that are likely to result in different emotional states. For example, sows can experience lameness and could show different facial expressions relating to pain before and after being given pain relief. Detecting positive emotional state is more novel but sows are highly food motivated and appear calm and content when satiated. They hope this mood could be reflected in sows facial expressions.
Images are then processed at UWE Bristol’s Centre for Machine Vision, where various state-of-the-art machine learning techniques are being developed to automatically identify different emotions conveyed by particular facial expressions. After validating these techniques, the team will develop the technology for on-farm use with commercial partners where individual sows in large herds will be monitored continuously.
Professor Melvyn Smith from UWE Bristol’s Centre for Machine Vision, part of the Bristol Robotics Laboratory, said: “Machine vision technology offers the potential to realise a low-cost, non-intrusive and practical means to biometrically identify individual animals on the farm. Our work has already demonstrated a 97% accuracy at facial recognition in pigs. Our next step will be, for the first time, to explore the potential for using machine vision to automatically recognise facial expressions that are linked with core emotion states, such as happiness or distress, in the identified pigs.”
Dr Emma Baxter from SRUC said: “Early identification of pig health issues gives farmers the potential to improve animal wellbeing by tackling any problems quickly and implementing tailored treatment for individuals. This will reduce production costs by preventing impact of health issues on performance.
“By focussing on the pig’s face, we hope to deliver a truly animal-centric welfare assessment technique, where the animal can “tell” us how it feels about its own individual experiences and environment. This allows insight into both short-term emotional reactions and long-term individual ‘moods’ of animals under our care.”
The study, which is being funded by the Biotechnology and Biological Sciences Research Council (BBSRC), is also being supported by industry stakeholders JSR Genetics Ltd and Garth Pig Practice as well as precision livestock specialists Agsenze.
Featured researcher: Professor Melvyn Smith
Melvyn L. Smith
is Professor of Machine Vision and Director of the Centre for Machine Vision
(CMV) at UWE Bristol. He is also a Chartered Engineer and an active member of
the IET.
Email: Melvyn.Smith@uwe.ac.uk Phone: +4411732 86358
Notes and links for editors:
https://bbsrc.ukri.org/research/
Relevant papers:
Hansen, M.F., Smith, M.L., Smith, L.N., Salter, M.G., Baxter, E.M., Farish, M. and Grieve, B., 2018. Towards on-farm pig face recognition using convolutional neural networks. Computers in Industry, 98, pp.145-152.
https://www.sciencedirect.com/science/article/pii/S0166361517304992
Camerlink, I., Coulange, E., Farish, M., Baxter, E.M. and Turner, S.P., 2018. Facial expression as a potential measure of both intent and emotion. Scientific reports, 8(1), p.17602.
https://www.nature.com/articles/s41598-018-35905-3
