Automated video-based pain recognition in cats using facial landmarks - Martvel, George; Lazebnik, Teddy; Feighelstein, Marcelo; Henze, Lea; Meller, Sebastian; Shimshoni, Ilan; Twele, Friederike; Schütter, Alexandra; Foraita, Nora; Kästner, Sabine; Finka, Lauren; Luna, Stelio P. L. [UNESP]; et al. (Springer Nature, 2024)
Affective states are reflected in the facial expressions of all mammals. Facial behaviors linked to pain have attracted most of the attention so far in non-human animals, leading to the development of numerous instruments for evaluating pain through facial expressions for various animal species. Nevertheless, manual facial expression analysis is susceptible to subjectivity and bias, is labor-intensive and often necessitates specialized expertise and training. This challenge has spurred a growing body of research into automated pain recognition, which has been explored for multiple species, including cats. In our previous studies, we have presented and studied artificial intelligence (AI) pipelines for automated pain recognition in cats using 48 facial landmarks grounded in cats’ facial musculature, as well as an automated detector of these landmarks. However, so far automated recognition of pain in cats used solely static information obtained from hand-picked single images of good quality. This study takes a significant step forward in fully automated pain detection applications by presenting an end-to-end AI pipeline that requires no manual efforts in the selection of suitable images or their landmark annotation. By working with video rather than still images, this new pipeline approach also optimises the temporal dimension of visual information capture in a way that is not practical to preform manually. The presented pipeline reaches over 70% and 66% accuracy respectively in two different cat pain datasets, outperforming previous automated landmark-based approaches using single frames under similar conditions, indicating that dynamics matter in cat pain recognition. We further define metrics for measuring different dimensions of deficiencies in datasets with animal pain faces, and investigate their impact on the performance of the presented pain recognition AI pipeline.