Investigating automatic detection of emotion in biometrically identified pig faces using machine learning

早期识别和解决猪健康问题可降低生产成本并改善动物健康。目前的人工目视检查只能提供间歇性和主观的信息，往往是在一组水平。对个体猪进行连续自动监测的能力允许持续了解个体，从而允许早期检测改变的健康和福利，从而允许更及时和更具成本效益的补救干预。超越这一目标的是开发和利用能够评估动物情感状态的技术，从而提供一个真正有洞察力的、以动物为中心的福利评估工具。这项研究特别新颖和及时，因为它使用高度创新的技术来开发以动物为中心的福利评估，理解动物的感知对社会和政策制定者非常重要。通过专注于面部表情的高度个体化测量，我们可以提供一种福利评估技术，这种技术超越了基本的监测，实际上可以推断出动物本身对特定体验的重要性。虽然没有负面的情感状态是，也应该是一个优先事项，我们还将包括特别新颖的工作，以检测面部表情的积极影响，从而更接近测量动物是否正在经历“美好生活”的最终目标。因此，该项目与BBSRC的战略优先事项相关：管理动物的福利，可持续地提高农业生产，动物健康和生物科学技术开发。机器视觉提供了一种低成本、非侵入性和实用的方法，可以通过生物识别识别单个动物，然后每天仅使用面部连续评估和记录它们的状况。通过采用最先进的机器学习技术，这样的系统将提供持续学习个人的能力，从而允许早期检测改变的健康/福利，个性化的干预阈值和定制的治疗方法。这种个性化的数据记录也可以通过与其他可测量的参数（例如个人食物和水摄入量、治疗历史、生长和体重增加）相关联，用于更广泛的精准农业背景，以便更好地优化农业生产效率。然而，最具创新性的应用是使用非侵入性技术来推断情感状态的潜力，从而可以洞察人类护理下动物的短期情感反应和长期个体“情绪”。该项目为畜牧业的多个利益相关者带来了明显的好处，因此吸引了有影响力的行业和技术公司的兴趣和支持，这些公司从一开始就热衷于参与这种创新方法的开发和应用。

Early identification and resolution of pig health issues results in reduced production costs and improved animal wellbeing. Current manual visual inspection offers only intermittent and subjective information often at a group level. A capacity for continuous automated monitoring of individual pigs allows on-going learning about individuals, and consequently allows early detection of altered health and welfare, so permitting more timely and cost effective remedial interventions. Going beyond that goal would be the development and harnessing of technology capable of assessing animal affective state thus offering a truly insightful, animal-centric welfare assessment tool.This research is particularly novel and timely as it uses highly innovative technologies to develop an animal-centric assessment of welfare, understanding that the sentience of animals is something of great importance to society and policy makers. By focussing on the highly individual measure of facial expression we can deliver a welfare assessment technique that goes beyond basic monitoring to actually inferring something about the importance the animals themselves place on particular experiences. Whilst the absence of negative affective state is and should be a priority we will also include particularly novel work to detect positive affect in facial expression, thus moving closer to the ultimate goal of measuring whether animals are experiencing "a good life". The project is therefore relevant to the BBSRC strategic priorities: welfare of managed animals, sustainably enhancing agricultural production, animal health and technology development for the biosciences. Machine vision offers the potential to realise a low-cost, non-intrusive and practical means to both biometrically identify individual animals and then assess and record their condition continuously each day using only the face. By employing state-of-the-art machine learning techniques, such a system would offer the capacity for on-going learning about individuals, and consequently allow for early detection of altered health/welfare, personalised thresholds for intervention, and tailored treatment approaches. Such individualised data recording can also be used in a wider precision farming context by association with other measurable parameters, such as individual food and water intake, treatment history, growth and weight gain, in order to better optimise farm production efficiency. The most innovative application, however, is the potential to use a non-intrusive technique to infer affective state, allowing insight into both short-term emotional reactions and longer-term individual "moods" of animals under human care. The project delivers clear benefits to multiple stakeholders within the livestock sector and therefore it has attracted the interest and support of influential industry and technology companies keen to be involved, from the start, in the development and application of such an innovative approach to animal welfare assessment.