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.
Abstract (translated)
抑郁是一种常见的心理健康障碍,它可能导致持续抑郁情绪和随之而来的症状,导致情感困扰。一种抑郁症状是掩盖性的抑郁,患者有意或无意通过外部乐观来掩盖真实的情感,从而复杂化、延迟诊断和治疗,并导致意外自杀。在本文中,我们提议通过面部微表情(FMEs)来诊断掩盖性的抑郁,以检测和识别背后的真实情感。然而,FMEs的极低强度和微妙性质使得它们的识别是一项困难的任务。我们提议基于面部地标的ROI approach来解决该挑战,并描述一种低成本、保护隐私的解决方案,可以在个人环境中(如在家里)使用便携式移动设备进行自我诊断。我们呈现了验证我们方法的结果和发现,并讨论了将这种方法应用于实际临床环境中的其他技术挑战和未来的研究方向。
URL
https://arxiv.org/abs/2307.15862
