University of Oulu

W. C. de Melo, E. Granger and A. Hadid, "Depression Detection Based on Deep Distribution Learning," 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 4544-4548. doi: 10.1109/ICIP.2019.8803467

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
Publish Date: 2020-02-18
Description:

Abstract

Major 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.

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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.
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