Orthopedic disorders are a common cause for euthanasia among horses, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle but long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeler to provide accurate ground-truth for the data. We show that transferring features from a dataset of horses with acute nociceptive pain (where labeling is less ambiguous) can aid the learning to recognize more complex orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on acute pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.
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