A Curriculum Learning Approach for Pain Intensity Recognition from Facial Expressions

Abstract

The high prevalence of chronic pain in society raises the need to develop new digital tools that can automatically and objectively assess pain intensity in individuals. These tools can contribute to an optimisation of clinical resources, as they offer cost-effective solutions for early detection, continuous monitoring, and treatment personalisation by utilising Artificial Intelligence techniques. In this work, we present our contribution to the Pain Intensity Estimation from Facial Expressions task of the EMOPAIN 2020 Challenge. Specifically, we compare the performance of Recurrent Neural Networks trained with standard or Curriculum Learning (CL) approaches to predict the pain intensity level of individuals reported in an 11-point scale from facial expressions. The results obtained using the test partition support the use of CL-based approaches in the automatic prediction of pain from facial features. The best model trained using a CL approach achieved a Concordance Correlation Coefficient (CCC) of 0.196 in the test partition, while the model trained using a standard approach, without CL, achieved a CCC of 0.174. In terms of CCC, these results respectively represent an improvement of 0.136 and 0.114 on the best results of the baseline system reported by the Challenge organisers using the test partition.