  Previous research on automatic pain estimation from facial expressions has
focused primarily on "one-size-fits-all" metrics (such as PSPI). In this work,
we focus on directly estimating each individual's self-reported visual-analog
scale (VAS) pain metric, as this is considered the gold standard for pain
measurement. The VAS pain score is highly subjective and context-dependent, and
its range can vary significantly among different persons. To tackle these
issues, 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 the
estimation of self-reported pain via a set of hand-crafted personal features
and multi-task learning. We show on the benchmark dataset for pain analysis
(The UNBC-McMaster Shoulder Pain Expression Archive) that the proposed
personalized model largely outperforms the traditional, unpersonalized models:
the intra-class correlation improves from a baseline performance of 19\% to a
personalized performance of 35\% while also providing confidence in the
model\textquotesingle s estimates -- in contrast to existing models for the
target task. Additionally, DeepFaceLIFT automatically discovers the
pain-relevant facial regions for each person, allowing for an easy
interpretation of the pain-related facial cues.
