期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 5, 页码 3314-3324出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2662199
关键词
Pain; Feature extraction; Hidden Markov models; Face; Estimation; Databases; Face recognition; Affective computing; computer applications; cybercare industry applications; human factors engineering in medicine and biology; medical services; monitoring; patient monitoring computers and information processing; pattern recognition
类别
资金
- Spanish Project (MINECO/FEDER) [TIN2015-65464-R]
- Generalitat de Catalunya [2016FI_B 01163]
- COST Action IC1307 iV&L Net (European Network on Integrating Vision and Language) through COST (European Cooperation in Science and Technology)
This paper proposes an automatic system for pain assessment, which outperforms the latest techniques by feeding the raw frames to deep learning models and considering the temporal relation and whole image. The research achieves competitive results in the UNBC-McMaster Shoulder Pain Expression Archive Database and the Cohn Kanade+ facial expression database.
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database.
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