Catching Elusive Depression via Facial Micro-Expression Recognition

TL;DR

This paper introduces a novel method for detecting concealed depression through facial micro-expression recognition using a landmark-based ROI approach, enabling private self-diagnosis on mobile devices.

Contribution

It presents a new facial micro-expression recognition technique tailored for concealed depression detection, with a portable, privacy-preserving implementation.

Findings

Validated the effectiveness of the ROI-based micro-expression recognition method

Demonstrated feasibility of self-diagnosis using mobile devices

Discussed technical challenges and future clinical applications

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 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.