Automatic pain estimation from facial expressions: a comparative analysis using off-the-shelf CNN architectures

Archive ouverte : Article de revue

El Morabit, Safaa | Rivenq, Atika | Zighem, Mohammed-En-Nadhir | Hadid, Abdenour | Ouahabi, Abdeldjalil | Taleb-Ahmed, Abdelmalik

Edité par HAL CCSD ; MDPI

International audience. Automatic pain recognition from facial expressions is a challenging problem that has attracted a significant attention from the research community. This article provides a comprehensive analysis on the topic by comparing some popular and Off-the-Shell CNN (Convolutional Neural Network) architectures, including MobileNet, GoogleNet, ResNeXt-50, ResNet18, and DenseNet-161. We use these networks in two distinct modes: stand alone mode or feature extractor mode. In stand alone mode, the models (i.e., the networks) are used for directly estimating the pain. In feature extractor mode, the “values” of the middle layers are extracted and used as inputs to classifiers, such as SVR (Support Vector Regression) and RFR (Random Forest Regression). We perform extensive experiments on the benchmarking and publicly available database called UNBC-McMaster Shoulder Pain. The obtained results are interesting as they give valuable insights into the usefulness of the hidden CNN layers for automatic pain estimation.

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