Pain intensity estimation by a self-taught selection of histograms of topographical features

作者：

Highlights：

• We introduce the Histogram of Topographical (HoT) features to address the variability in face images.

• We propose a semi-supervised, clustering-oriented, self-taught learning procedure.

• We propose a machine learning based, temporal filtering to increase the overall accuracy.

• A system for face dynamic analysis that applied to pain intensity estimation leads to qualitative results.

摘要

•We introduce the Histogram of Topographical (HoT) features to address the variability in face images.•We propose a semi-supervised, clustering-oriented, self-taught learning procedure.•We propose a machine learning based, temporal filtering to increase the overall accuracy.•A system for face dynamic analysis that applied to pain intensity estimation leads to qualitative results.

论文关键词：Histograms of Topographical (HoT) features,Spectral regression,Transfer learning,Temporal filtering,Continuous pain intensity estimation

论文评审过程：Received 2 December 2015, Revised 8 April 2016, Accepted 1 August 2016, Available online 16 September 2016, Version of Record 3 October 2016.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.08.014