Development of an Automated Pain Facial Expression Detection System for Sheep (Ovis Aries)

SIMPLE SUMMARY: Detecting signs of pain in sheep is a challenging problem, as they are a prey species and would usually try to hide any signs that they are unwell or injured. This means that treating ill or injured sheep and preventing any further spread of contagious diseases such as footrot can be slow. The recent development and publication of a Sheep Pain Facial Expression Scale (SPFES) has provided a tool to reliably detect pain in this species. However, due to the increase in intensification in farming and larger flock sizes being cared for by individual farmers, there is less time to spend monitoring sheep for changes in behaviour that may indicate illness or injury. Having an automated system that could detect changes in the facial expression of individual sheep would mean that farmers could receive information directly about particular individuals that need assessment. This would allow treatment to be provided in a timely and direct manner, reducing suffering. We have been developing the SPFES further in order for it to become an automated system. In this paper, we present our novel framework that integrates SPFES concepts with automatic facial expression analysis technologies. 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.