Catching Elusive Depression via Facial Micro-Expression Recognition
This addresses the problem of delayed diagnosis and treatment for patients with concealed depression, though it appears incremental as it builds on existing micro-expression recognition methods.
The paper tackles diagnosing concealed depression by using facial micro-expressions to detect underlying emotions, proposing a facial landmark-based ROI approach that enables low-cost, privacy-preserving self-diagnosis via mobile devices.
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.