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Creators/Authors contains: "de Sa, V.R."

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  1. ; (, 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC))
    null (Ed.)
    Pain is a personal, subjective experience, and the current gold standard to evaluate pain is the Visual Analog Scale (VAS), which is self-reported at the video level. One problem with the current automated pain detection systems is that the learned model doesn’t generalize well to unseen subjects. In this work, we propose to improve pain detection in facial videos using individual models and uncertainty estimation. For a new test video, we jointly consider which individual models generalize well generally, and which individual models are more similar/accurate to this test video, in order to choose the optimal combination of individual models and get the best performance on new test videos. We show on the UNBCMcMaster Shoulder Pain Dataset that our method significantly improves the previous state-of-the-art performance. 
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    Accepted Manuscript Full Text Available