Open AccessElderly Depression Recognition Based on Facial Micro-Expression Extraction
Author(s) -
Wei Huang
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380423
Subject(s) - expression (computer science) , depression (economics) , hilbert–huang transform , feature extraction , pattern recognition (psychology) , modal , artificial intelligence , feature (linguistics) , facial expression recognition , computer science , facial expression , psychology , facial recognition system , computer vision , linguistics , chemistry , philosophy , filter (signal processing) , polymer chemistry , economics , macroeconomics , programming language
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.