Citation

Huang W. Traitement du Signal 2021; 38(4): 1123-1130.

Copyright

(Copyright © 2021)

DOI

10.18280/ts.380423

PMID

unavailable

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.

Language: en

Keywords

Mental disorders; Mental health; Extraction; Face recognition; Death rates; Deep learning; Health disorders; Deep learning (DL); Elderly depression recognition; Expression feature extraction; Expression recognition; Lower precision; Micro-expression; Micro-expressions