@article{ref1,
title="Elderly depression recognition based on facial micro-expression extraction",
journal="Traitement du Signal",
year="2021",
author="Huang, W.",
volume="38",
number="4",
pages="1123-1130",
abstract="Depression leads to a high suicide rate and a high death rate. But the disease can be cured if recognized in time. At present, there are only a few low-precision methods for recognizing mental health or mental disorder. Therefore, this paper attempts to recognize elderly depression by extracting facial micro-expressions. Firstly, a micro-expression recognition model was constructed for elderly depression recognition. Then, a jump connection structure and a feature fusion module were introduced to VGG-16 model, realizing the extraction and classification of micro-expression features. After that, a quantitative evaluation approach was proposed for micro-expressions based on the features of action units, which improves the recognition accuracy of elderly depression expressions. Finally, the advanced features related to the dynamic change rate of depression micro-expressions were constructed, and subjected to empirical modal decomposition (EMD) and Hilbert analysis. The effectiveness of our algorithm was proved through experiments. © 2021 Lavoisier. All rights reserved.<p /><p>Language: en</p>",
language="en",
issn="0765-0019",
doi="10.18280/ts.380423",
url="http://dx.doi.org/10.18280/ts.380423"
}