11 July 2019 Pain-awareness multistream convolutional neural network for pain estimation
Dong Huang, Zhaoqiang Xia, Lei Li, Kunwei Wang, Xiaoyi Feng
Author Affiliations +
Abstract
In medical institutions, pain is one of the important clues for patients to transmit their conditions effectively, which makes the estimation of pain status an exceedingly important task. Of late, many methods have been proposed to address this task. However, most of them estimate the pain from entire face images or videos instead of paying more attention to the regions most relevant to pain. We propose a pain-awareness multistream convolutional neural network (CNN) for pain estimation. Specifically, we separate the regions most relevant to the pain expression, and the multistream CNN is used to learn the corresponding pain-awareness features. These features are combined into pain features with adaptive weights to estimate the intensity of pain. Extensive experiments on the publicly available pain database indicate that our multistream CNN-based method has achieved inspiring results compared to the state-of-the-art technologies.
© 2019 SPIE and IS&T 1017-9909/2019/$28.00 © 2019 SPIE and IS&T
Dong Huang, Zhaoqiang Xia, Lei Li, Kunwei Wang, and Xiaoyi Feng "Pain-awareness multistream convolutional neural network for pain estimation," Journal of Electronic Imaging 28(4), 043008 (11 July 2019). https://doi.org/10.1117/1.JEI.28.4.043008
Received: 3 April 2019; Accepted: 18 June 2019; Published: 11 July 2019
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CITATIONS
Cited by 10 scholarly publications.
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KEYWORDS
Databases

Convolutional neural networks

Feature extraction

Gold

Video

Eye models

Eye

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