Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
This addresses the need for efficient pain score acquisition in large-scale studies by providing a personalized automatic estimation method, though it is incremental as it builds on existing pain analysis techniques.
The paper tackles the problem of automatically estimating self-reported pain intensity from facial expressions by proposing a two-stage learning approach that first estimates PSPI levels using RNNs and then uses personalized HCRFs with a facial expressiveness score to estimate VAS, showing benefits over non-personalized methods on a benchmark dataset.
Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.