Analysing pain in sheep
Marwa Mahmoud, Quentin Stafford-Fraser, Andy Zhang, Heng Yang, Yiting Lu, & Peter Robinson
Pain indication in sheep
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Assessing pain levels in animals is a crucial, but time-consuming process in maintaining their welfare. Facial expressions in sheep are an efficient and reliable indicator of pain levels. The ovine affect project builds on our earlier work in affective computing, and especially on our work in face tracking, to investigate applications in animal welfare. We have extended techniques for recognising human facial expressions to encompass facial action units in sheep, which can then facilitate automatic estimation of pain levels. Our multi-level approach starts with detection of sheep faces, localisation of facial landmarks, normalisation and then extraction of facial features. These are described using Histogram of Oriented Gradients, and then classified using Support Vector Machines. Our experiments show an overall accuracy of 67% on sheep Action Units classification. We argue that with more data, our approach on automated pain level assessment can be generalised to other animals.
Publications
- Human and sheep facial landmarks localisation by triplet interpolated features, Andy Zhang, Heng Yang, Peter Robinson. IEEE Winter Conference on Applications of Computer Vision, Lake Placid, NY, March 2016.
- Estimating sheep pain level using facial action unit detection, Yiting Lu, Marwa Mahmoud, Peter Robinson. IEEE Conference on Automatic Face and Gesture Recognition, Washington DC, May 2017.
Other papers are in various stages of submission and publication.