Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/FG.2017.56 | 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) |
Keywords | Field | DocType |
sheep pain level estimation,facial action unit detection,human facial expressions recognition,automatic pain level estimation,multilevel approach,sheep face detection,facial landmark localisation,facial feature extraction,histogram of oriented gradients,support vector machines,action units classification,automated pain level assessment | Computer vision,Computer science,Support vector machine,Facial expression,Histogram of oriented gradients,Artificial intelligence,Assessing Pain,Pain level | Conference |
ISSN | ISBN | Citations |
2326-5396 | 978-1-5090-4024-7 | 4 |
PageRank | References | Authors |
0.41 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiting Lu | 1 | 6 | 1.84 |
| Marwa Mahmoud | 2 | 5 | 1.44 |
| Peter Robinson | 3 | 1438 | 129.42 |