Depression detection based on deep distribution learning |
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Author: | de Melo, Wheidima Carneiro1; Granger, Eric2; Hadid, Abdenour1 |
Organizations: |
1Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland 2LIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 3.3 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202002185698 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2019
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Publish Date: | 2020-02-18 |
Description: |
AbstractMajor depressive disorder is among the most common and harmful mental health problems. Several deep learning architectures have been proposed for video-based detection of depression based on the facial expressions of subjects. To predict the depression level, these architectures are often modeled for regression with Euclidean loss. Consequently, they do not leverage the data distribution, nor explore the ordinal relationship between facial images and depression levels, and have limited robustness to noisy and uncertain labeling. This paper introduces a deep learning architecture for accurately predicting depression levels through distribution learning. It relies on a new expectation loss function that allows to estimate the underlying data distribution over depression levels, where expected values of the distribution are optimized to approach the ground-truth levels. The proposed approach can produce accurate predictions of depression levels even under label uncertainty. Extensive experiments on the AVEC2013 and AVEC2014 datasets indicate that the proposed architecture represents an effective approach that can outperform state-of-the-art techniques. see all
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Series: |
IEEE International Conference on Image Processing |
ISSN: | 1522-4880 |
ISSN-E: | 2381-8549 |
ISSN-L: | 1522-4880 |
ISBN: | 978-1-5386-6249-6 |
ISBN Print: | 978-1-5386-6250-2 |
Pages: | 4544 - 4548 |
Article number: | 8803467 |
DOI: | 10.1109/ICIP.2019.8803467 |
OADOI: | https://oadoi.org/10.1109/ICIP.2019.8803467 |
Host publication: |
26th IEEE International Conference on Image Processing, ICIP 2019, 22-25 Sept. 2019, Taipei, Taiwan |
Conference: |
IEEE International Conference on Image Processing |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
213 Electronic, automation and communications engineering, electronics |
Subjects: | |
Funding: |
The financial support of the Academy of Finland and Infotech Oulu is acknowledged. W. C. de Melo would like to thank the State University of Amazonas for its support. |
Copyright information: |
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