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

使用机器学习研究生物识别猪脸中的情绪自动检测

• 批准号: BB/S002138/1
• 负责人: Melvyn Smith
• 金额: $ 11.51万
• 依托单位: University of the West of England
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FS002138%2F1
• 关键词: Investigating; automatic; detection; emotion; biometrically

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.

猪健康问题的早期识别和解决导致生产成本降低和改善动物健康。当前的手动视觉检查通常仅在小组级别提供间歇性和主观信息。对单个猪进行连续自动监测的能力可以持续学习个人，因此可以尽早发现健康和福利改变，因此允许更及时，更具成本效益的补救干预措施。超越这个目标的是能够评估动物情感状态的技术的发展和利用，从而提供了一种真正有见地的，以动物为中心的福利评估工具。这项研究尤其及时，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，并了解动物对社会和政策制定者和政策制定者的重要性。通过专注于高度个性化的面部表达量度，我们可以提供一种福利评估技术，该技术超出了基本的监测，而可以实际推断出动物本身对特定经验的重要性。尽管缺乏负面情感状态是并且应该是优先事项，但我们还将特别包括新的工作，以检测面部表达的积极影响，从而更接近衡量动物是否经历“美好生活”的最终目标。因此，该项目与BBSRC战略重点有关：托管动物的福利，可持续增强生物科学的农业生产，动物健康和技术发展。 Machine Vision提供了实现低成本，非侵入性和实用性手段的潜力，以生物识别单个动物，然后仅使用面部每天连续评估和记录其状况。通过采用最先进的机器学习技术，这样的系统将提供有关个人持续学习的能力，因此可以尽早发现健康/福利，个性化阈值以进行干预以及量身定制的治疗方法。这种个性化的数据记录也可以通过与其他可测量参数（例如个体食物和水的摄入量，治疗历史，生长和体重增加）相结合，以更好地优化农场生产效率，从而在更广泛的耕作环境中使用。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，从而深入了解短期情绪反应和人类护理中动物的长期个人“情绪”。该项目为牲畜部门内的多个利益相关者带来了明显的利益，因此，从一开始就吸引了有影响力的行业和技术公司的兴趣和支持，从一开始就开始发展和应用这种创新的动物福利评估方法。

项目成果

Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs

• DOI: 10.3390/agriculture11090847
• 发表时间: 2021-09-01
• 期刊: AGRICULTURE-BASEL
• 影响因子: 3.6
• 作者: Hansen, Mark F.;Baxter, Emma M.;Smith, Lyndon N.
• 通讯作者: Smith, Lyndon N.

Towards on-farm pig face recognition using convolutional neural networks

• DOI: 10.1016/j.compind.2018.02.016
• 发表时间: 2018-06-01
• 期刊: COMPUTERS IN INDUSTRY
• 影响因子: 10
• 作者: Hansen, Mark E.;Smith, Melvyn L.;Grieve, Bruce
• 通讯作者: Grieve, Bruce

Contactless robust 3D palm-print identification using photometric stereo

• DOI: 10.1117/12.2595439
• 发表时间: 2021-08
• 作者: Lyndon N. Smith;Max P. Langhof;M. Hansen;Melvyn L. Smith

Deep 3D Face Recognition using 3D Data Augmentation and Transfer Learning

使用 3D 数据增强和迁移学习进行深度 3D 人脸识别

• 发表时间: 2020
• 作者: Lyndon Smith

Surface Normals Based Landmarking for 3D Face Recognition Using Photometric Stereo Captures

使用光度立体捕获进行 3D 人脸识别的基于表面法线的地标

• DOI: 10.1145/3345336.3345339
• 发表时间: 2019
• 作者: Gao J