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Automated recognition of pain in cats

By June 10th, 2022July 11th, 2022No Comments

Document type: Scientific article published in Scientific Reports

Authors: Marcelo Feighelstein, Ilan Shimshoni, Lauren R. Finka, Stelio P. L. Luna, Daniel S. Mills, Anna Zamansky 

  

Preview: 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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