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. 2022 Jun 10;12(1):9575.
doi: 10.1038/s41598-022-13348-1.

Automated recognition of pain in cats

Marcelo Feighelstein  1 , Ilan Shimshoni  1 , Lauren R Finka  2 , Stelio P L Luna  3 , Daniel S Mills  4 , Anna Zamansky  5
Affiliations

Automated recognition of pain in cats

Marcelo Feighelstein et al. Sci Rep. .

Abstract

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.

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Figures

Figure 1
Figure 1
Mirror image of cat’s face, depicting placement of the 48 facial landmarks. Landmarks appear contralateral to their origin, as they would when directly observing the cat’s face.
Figure 2
Figure 2
Image before and after alignment.
Figure 3
Figure 3
Landmark transformation into vectors, and division into four regions of interest according to the Felice Grimace Scale.
Figure 4
Figure 4
Preprocessing pipeline for model 1.
Figure 5
Figure 5
Preprocessing pipeline for model 2.
See this image and copyright information in PMC

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