@inproceedings{9e490adc97124236b1d574414b0dd715,
title = "Detection of mood disorder using modulation spectrum of facial action unit profiles",
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.",
author = "Yang, {Tsung Hsien} and Wu, {Chung Hsien} and Su, {Ming Hsiang} and Chang, {Chia Cheng}",
year = "2018",
month = feb,
day = "1",
doi = "10.1109/ICOT.2016.8278966",
language = "English",
series = "2016 International Conference on Orange Technologies, ICOT 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5--8",
booktitle = "2016 International Conference on Orange Technologies, ICOT 2016",
address = "United States",
note = "2016 International Conference on Orange Technologies, ICOT 2016 ; Conference date: 18-12-2016 Through 20-12-2016",
}