Estimating Sheep Pain Level Using Facial Action Unit Detection

Lu, Y., Mahmoud, M. and Robinson, P. (2017) Estimating Sheep Pain Level Using Facial Action Unit Detection. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, D.C., USA, 30 May - 03 Jun 2017, pp. 394-399. ISBN 9781509040230 (doi: 10.1109/FG.2017.56)

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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. In this paper, 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.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Mahmoud, Dr Marwa
Authors: Lu, Y., Mahmoud, M., and Robinson, P.
College/School:College of Science and Engineering > School of Computing Science

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