Autors: Neshov, N. N., Manolova, A. H.
Title: Pain detection from facial characteristics using supervised descent method
Keywords: pain detection, pain intensity estimation, supervised descen

Abstract: In this paper we propose an algorithm for both automatic pain recognition (i.e. pain/no pain presence in human) and continuous pain intensity estimation based on facial expression analysis. To locate specific landmarks in the face we used Supervised Descent Method (SDM) and then extract feature vectors using Scale Invariant Feature Transform (SIFT). For the recognition task we build a classier based on Support Vector Machines (SVM) and for the continuous pain intensity estimation task we trained linear regressor. The experiments with patients with shoulder pain show very good recognition rate (more than 95.7%). For the pain intensity estimation we reached an average Mean Squared Error of 1.28 and Correlation coefficient of 0.59. The recorded results demonstrate performance that exceeds state-of-the-art results on a standard data set.

References

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    2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol. 1, pp. 251-256, 2015, Poland, IEEE, DOI 10.1109/IDAACS.2015.7340738

    Copyright IEEE

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