Analysis And Recognition Of Pain In 2d Face Images Of Full Term And Healthy Newborns

Gilberto F. Teruel; Tatiany M. Heiderich; Ruth Guinsburg; Carlos E. Thomaz

doi:10.5753/eniac.2018.4419

Gilberto F. Teruel FEI
Tatiany M. Heiderich UNIFESP
Ruth Guinsburg UNIFESP
Carlos E. Thomaz UNIFESP

DOI: https://doi.org/10.5753/eniac.2018.4419

Resumo

This paper proposes a sequence of computational procedures for detecting, interpreting and classifying patterns in frontal two-dimensional images of faces for automatic recognition of pain in newborns. Using data transformation and extraction of statistical characteristics from a real-life, healthy-term newborn image database, it was possible to interpret and model the subjectivity of trained health professionals, quantifying human knowledge in the task of recognizing pain enabling automatic identification. These results were compared with NFCS based classifications by the same professionals of the same images.

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