Abstract
Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions through exterior optimism, thereby complicating and delaying diagnosis and treatment and leading to unexpected suicides. In this article, we propose to diagnose concealed depression by using facial micro-expressions (FMEs) to detect and recognize underlying true emotions. However, the extremely low intensity and subtle nature of FMEs make their recognition a tough task. We propose a facial landmark-based Region-of-Interest (ROI) approach to address the challenge and describe a low-cost and privacy-preserving solution that enables self-diagnosis using portable mobile devices in a personal setting (e.g., at home). We present results and findings that validate our method and discuss other technical challenges and future directions in applying such techniques to real clinical settings.
Recommended Citation
X. Chen and T. T. Luo, "Catching Elusive Depression Via Facial Micro-Expression Recognition," IEEE Communications Magazine, vol. 61, no. 10, pp. 30 - 36, Institute of Electrical and Electronics Engineers, Oct 2023.
The definitive version is available at https://doi.org/10.1109/MCOM.001.2300003
Department(s)
Computer Science
International Standard Serial Number (ISSN)
1558-1896; 0163-6804
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Oct 2023
