Catching Elusive Depression via Facial Micro-Expression Recognition
Xiaohui Chen Tie Luo
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 paper, 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.