Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions
Name
1706.07154.pdf
Description
Submitted version
Size
2.21 MB
Format
Adobe PDF
Checksum (MD5)
37c66909ab67060ccb3fead5c37faeb0
Author(s)
Martinez, Daniel Lopez
Rudovic, Ognjen
Picard, Rosalind W.
Date Issued
2017
Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
© 2017 IEEE. 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 partictipants' 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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/CVPRW.2017.286
