University of Oulu

M. Tavakolian and A. Hadid, "Deep Spatiotemporal Representation of the Face for Automatic Pain Intensity Estimation," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, 2018, pp. 350-354,

Deep spatiotemporal representation of the face for automatic pain intensity estimation

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Author: Tavakolian, Mohammad1; Hadid, Abdenour1
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link:
Language: English
Published: IEEE Computer Society, 2018
Publish Date: 2020-04-14


Automatic pain intensity assessment has a high value in disease diagnosis applications. Inspired by the fact that many diseases and brain disorders can interrupt normal facial expression formation, we aim to develop a computational model for automatic pain intensity assessment from spontaneous and micro facial variations. For this purpose, we propose a 3D deep architecture for dynamic facial video representation. The proposed model is built by stacking several convolutional modules where each module encompasses a 3D convolution kernel with a fixed temporal depth, several parallel 3D convolutional kernels with different temporal depths, and an average pooling layer. Deploying variable temporal depths in the proposed architecture allows the model to effectively capture a wide range of spatiotemporal variations on the faces. Extensive experiments on the UNBC-McMaster Shoulder Pain Expression Archive database show that our proposed model yields in a promising performance compared to the state-of-the-art in automatic pain intensity estimation.

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Series: International Conference on Pattern Recognition
ISSN: 1051-4651
ISSN-L: 1051-4651
ISBN: 978-1-5386-3788-3
ISBN Print: 978-1-5386-3789-0
Pages: 350 - 354
DOI: 10.1109/ICPR.2018.8545324
Host publication: 2018 24th International Conference on Pattern Recognition (ICPR)
Conference: International Conference on Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
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