Abstract
In mood disorder diagnosis, bipolar disorder (BD) patients are often misdiagnosed as unipolar depression (UD) on initial presentation. It is crucial to establish an accurate distinction between BD and UD to make an accurate and early diagnosis, leading to improvements in treatment. In this work, facial expressions of the subjects are collected when they were watching the eliciting emotional video clips. In mood disorder detection, first, facial features extracted from the DISFA database are used to train a support vector machine (SVM) for generating facial action unit (AU) profiles. The modulation spectrum characterizing the fluctuation of AU profile sequence over a video segment are further extracted and then used for mood disorder detection using an ANN model. Comparative experiments clearly show the promising advantage of the modulation spectrum features for mood disorder detection.
| Original language | English |
|---|---|
| Title of host publication | 2016 International Conference on Orange Technologies, ICOT 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5-8 |
| Number of pages | 4 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538648315 |
| DOIs | |
| Publication status | Published - 2018 Feb 1 |
| Event | 2016 International Conference on Orange Technologies, ICOT 2016 - Melbourne, Australia Duration: 2016 Dec 18 → 2016 Dec 20 |
Other
| Other | 2016 International Conference on Orange Technologies, ICOT 2016 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 16-12-18 → 16-12-20 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Behavioral Neuroscience
- Cognitive Neuroscience
Cite this
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Detection of mood disorder using modulation spectrum of facial action unit profiles. / Yang, Tsung Hsien; Wu, Chung-Hsien; Su, Ming Hsiang; Chang, Chia Cheng.
2016 International Conference on Orange Technologies, ICOT 2016. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 5-8 8278966.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Detection of mood disorder using modulation spectrum of facial action unit profiles
AU - Yang, Tsung Hsien
AU - Wu, Chung-Hsien
AU - Su, Ming Hsiang
AU - Chang, Chia Cheng
PY - 2018/2/1
Y1 - 2018/2/1
N2 - In mood disorder diagnosis, bipolar disorder (BD) patients are often misdiagnosed as unipolar depression (UD) on initial presentation. It is crucial to establish an accurate distinction between BD and UD to make an accurate and early diagnosis, leading to improvements in treatment. In this work, facial expressions of the subjects are collected when they were watching the eliciting emotional video clips. In mood disorder detection, first, facial features extracted from the DISFA database are used to train a support vector machine (SVM) for generating facial action unit (AU) profiles. The modulation spectrum characterizing the fluctuation of AU profile sequence over a video segment are further extracted and then used for mood disorder detection using an ANN model. Comparative experiments clearly show the promising advantage of the modulation spectrum features for mood disorder detection.
AB - In mood disorder diagnosis, bipolar disorder (BD) patients are often misdiagnosed as unipolar depression (UD) on initial presentation. It is crucial to establish an accurate distinction between BD and UD to make an accurate and early diagnosis, leading to improvements in treatment. In this work, facial expressions of the subjects are collected when they were watching the eliciting emotional video clips. In mood disorder detection, first, facial features extracted from the DISFA database are used to train a support vector machine (SVM) for generating facial action unit (AU) profiles. The modulation spectrum characterizing the fluctuation of AU profile sequence over a video segment are further extracted and then used for mood disorder detection using an ANN model. Comparative experiments clearly show the promising advantage of the modulation spectrum features for mood disorder detection.
UR - http://www.scopus.com/inward/record.url?scp=85051012172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051012172&partnerID=8YFLogxK
U2 - 10.1109/ICOT.2016.8278966
DO - 10.1109/ICOT.2016.8278966
M3 - Conference contribution
VL - 2018-January
SP - 5
EP - 8
BT - 2016 International Conference on Orange Technologies, ICOT 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -