Automated recognition of pain in cats

Feighelstein, Marcelo, Shimshoni, Ilan, Finka, Lauren , Luna, Stelio, Mills, Daniel and Zamansky, Anna (2022) Automated recognition of pain in cats. Scientific Reports, 12 (9575). pp. 1-10. ISSN 2045-2322

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Automated recognition of pain in cats
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Feighelstein, M. et al. (2022) Automated recognition of pain in cats. Sci Rep 12(9575) 1-10..pdf - Whole Document
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Facial expressions in non-human animals are closely linked to their internal affective states, with the majority of empirical work focusing on facial shape changes associated with pain. However, existing tools for facial expression analysis are prone to human subjectivity and bias, and in many cases also require special expertise and training. This paper presents the first comparative study of two different paths towards automatizing pain recognition in facial images of domestic short haired cats (n = 29), captured during ovariohysterectomy at different time points corresponding to varying intensities of pain. One approach is based on convolutional neural networks (ResNet50), while the other—on machine learning models based on geometric landmarks analysis inspired by species specific Facial Action Coding Systems (i.e. catFACS). Both types of approaches reach comparable accuracy of above 72%, indicating their potential usefulness as a basis for automating cat pain detection from images.

Keywords:Animal Behaviour, Pain
Subjects:D Veterinary Sciences, Agriculture and related subjects > D300 Animal Science
D Veterinary Sciences, Agriculture and related subjects > D390 Veterinary Sciences not elsewhere classified
Divisions:College of Science > School of Life and Environmental Sciences > Department of Life Sciences
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ID Code:53474
Deposited On:17 Feb 2023 16:46

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