University of Oulu

M. Tavakolian and A. Hadid, "Deep Binary Representation of Facial Expressions: A Novel Framework for Automatic Pain Intensity Recognition," 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, 2018, pp. 1952-1956. doi: 10.1109/ICIP.2018.8451681

Deep binary representation of facial expressions : a novel framework for automatic pain intensity recognition

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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, 1.7 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2019-02-26


Automatic pain assessment is crucial in clinical diagnosis. Experiencing pain causes deformations in the facial structure resulting in different spontaneous facial expressions. In this paper, we aim to represent the facial expressions as a compact binary code for classification of different pain intensity levels. We divide a given face video into non-overlapping equal-length segments. Using a Convolutional Neural Network (CNN), we extract features from randomly sampled frames from all segments. The obtained features are aggregated by exploiting statistics to incorporate low-level visual patterns and high-level structural information. Finally, this processed information is encoded using a deep network to obtain a single binary code such that videos with the same pain intensity level have smaller Hamming distance than those of different levels. Extensive experiments on the publicly available UNBC-McMaster database demonstrates that our proposed method achieves superior performance compared to the state-of-the-art.

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Series: IEEE International Conference on Image Processing
ISSN: 1522-4880
ISSN-E: 2381-8549
ISSN-L: 1522-4880
ISBN: 978-1-4799-7061-2
ISBN Print: 978-1-4799-7062-9
Pages: 1952 - 1956
DOI: 10.1109/ICIP.2018.8451681
Host publication: 2018 25th IEEE International Conference on Image Processing (ICIP)
Conference: IEEE International Conference on Image Processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: The financial support of the Academy of Finland and Infotech Oulu is acknowledged.
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