Spatio-temporal Pain Recognition in CNN-Based Super-Resolved Facial Images

  • Marco Bellantonio
  • Mohammad A. Haque
  • Pau Rodriguez
  • Kamal Nasrollahi
  • Taisi Telve
  • Sergio Escalera
  • Jordi Gonzalez
  • Thomas B. Moeslund
  • Pejman Rasti
  • Gholamreza Anbarjafari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10165)

Abstract

Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.

Keywords

Super-Resolution Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) Pain detection 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marco Bellantonio
    • 1
  • Mohammad A. Haque
    • 2
  • Pau Rodriguez
    • 1
  • Kamal Nasrollahi
    • 2
  • Taisi Telve
    • 3
  • Sergio Escalera
    • 1
  • Jordi Gonzalez
    • 1
  • Thomas B. Moeslund
    • 2
  • Pejman Rasti
    • 3
  • Gholamreza Anbarjafari
    • 3
  1. 1.Computer Vision Center (UAB)University of BarcelonaBarcelonaSpain
  2. 2.Visual Analysis of People (VAP) LaboratoryAalborg UniversityAalborgDenmark
  3. 3.iCV Research Group, Institute of TechnologyUniversity of TartuTartuEstonia

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