Catching Elusive Depression via Facial Micro-Expression Recognition

• URL: http://arxiv.org/abs/2307.15862v1
• Date: Sat, 29 Jul 2023 01:51:17 GMT
• Title: Catching Elusive Depression via Facial Micro-Expression Recognition
• Authors: Xiaohui Chen and Tie Luo
• Abstract summary: Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions. We propose to diagnose concealed depression by using facial micro-expressions to detect and recognize underlying true emotions.
• Score: 17.236980932143855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detection [18.797661194307683]
  Previous studies have demonstrated that emotional features from a single acoustic sentiment label can enhance depression diagnosis accuracy. Individuals with depression might convey negative emotional content in an unexpectedly calm manner. This work is the first to incorporate emotional expression inconsistency information into depression detection.
  arXiv  Detail & Related papers  (2024-12-09T02:52:52Z)
• Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking [27.96718892323191]
  Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection. Recent work focuses on combining task-oriented dialogue and chitchat to simulate the interview-based depression diagnosis. No explicit framework has been explored to guide the dialogue, which results in some useless communications.
  arXiv  Detail & Related papers  (2024-03-12T07:17:01Z)
• Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
  Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• CauESC: A Causal Aware Model for Emotional Support Conversation [79.4451588204647]
  Existing approaches ignore the emotion causes of the distress. They focus on the seeker's own mental state rather than the emotional dynamics during interaction between speakers. We propose a novel framework CauESC, which firstly recognizes the emotion causes of the distress, as well as the emotion effects triggered by the causes.
  arXiv  Detail & Related papers  (2024-01-31T11:30:24Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Climate and Weather: Inspecting Depression Detection via Emotion Recognition [25.290414205116107]
  This paper uses pretrained features extracted from the emotion recognition model for depression detection to form multimodal depression detection. The proposed emotion transfer improves depression detection performance on DAIC-WOZ as well as increases the training stability.
  arXiv  Detail & Related papers  (2022-04-29T13:44:22Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• Examining the Role of Mood Patterns in Predicting Self-Reported Depressive symptoms [4.564132389935269]
  Depression is the leading cause of disability worldwide. Initial efforts to detect depression signals from social media posts have shown promising results. In this work, we attempt to enrich current technology for detecting symptoms of potential depression by constructing a'mood profile' for social media users.
  arXiv  Detail & Related papers  (2020-06-14T12:48:43Z)
• Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews [0.0]
  Depression is a mental disorder that impacts not only the subject's mood but also the use of language. We use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica.
  arXiv  Detail & Related papers  (2020-06-04T20:55:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.