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Previous research on automatic pain estimation from facial expressions hasfocused primarily on "one-size-fits-all" metrics (such as PSPI). In this work,we focus on directly estimating each individual's self-reported visual-analogscale (VAS) pain metric, as this is considered the gold standard for painmeasurement. The VAS pain score is highly subjective and context-dependent, andits range can vary significantly among different persons. To tackle theseissues, we propose a novel two-stage personalized model, named DeepFaceLIFT,for automatic estimation of VAS. This model is based on (1) Neural Network and(2) Gaussian process regression models, and is used to personalize theestimation of self-reported pain via a set of hand-crafted personal featuresand multi-task learning. We show on the benchmark dataset for pain analysis(The UNBC-McMaster Shoulder Pain Expression Archive) that the proposedpersonalized model largely outperforms the traditional, unpersonalized models:the intra-class correlation improves from a baseline performance of 19\% to apersonalized performance of 35\% while also providing confidence in themodel\textquotesingle s estimates -- in contrast to existing models for thetarget task. Additionally, DeepFaceLIFT automatically discovers thepain-relevant facial regions for each person, allowing for an easyinterpretation of the pain-related facial cues.

DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain