Automated detection of pain levels using deep feature extraction from shutter blinds‑based dynamic‑sized horizontal patches with facial images
Metadatos
Afficher la notice complèteEditorial
Nature
Date
2022-10-14Referencia bibliográfica
Barua, P.D... [et al.]. Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images. Sci Rep 12, 17297 (2022). [https://doi.org/10.1038/s41598-022-21380-4]
Résumé
Pain intensity classification using facial images is a challenging problem in computer vision research.
This work proposed a patch and transfer learning-based model to classify various pain intensities
using facial images. The input facial images were segmented into dynamic-sized horizontal patches
or “shutter blinds”. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used
to generate deep features from the shutter blinds and the undivided resized segmented input facial
image. The most discriminative features were selected from these deep features using iterative
neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor
classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model
was trained and tested on datasets derived from two public databases—University of Northern
British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of
Spontaneous Facial Action Database—which both comprised four pain intensity classes that had
been labeled by human experts using validated facial action coding system methodology. Our shutter
blinds-based classification model attained more than 95% overall accuracy rates on both datasets.
The excellent performance suggests that the automated pain intensity classification model can be
deployed to assist doctors in the non-verbal detection of pain using facial images in various situations
(e.g., non-communicative patients or during surgery). This system can facilitate timely detection and
management of pain.