Development of an automated pain facial expression detection system for sheep (Ovis Aries)

McLennan, K. and Mahmoud, M. (2019) Development of an automated pain facial expression detection system for sheep (Ovis Aries). Animals, 9(4), 196. (doi: 10.3390/ani9040196) (PMID:31027279) (PMCID:PMC6523241)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Mahmoud, Dr Marwa

Creator Roles

Mahmoud, M.: Writing – original draft, Writing – review and editing

• Authors: McLennan, K., and Mahmoud, M.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Animals
• Publisher: MDPI
• ISSN: 2076-2615
• ISSN (Online): 2076-2615
• Published Online: 25 April 2019
• Copyright Holders: Copyright © 2019 The Authors
• First Published: First published in Animals 9(4): 196
• Publisher Policy: Reproduced under a Creative Commons License