Can body expressions contribute to automatic depression analysis?

Jyoti Joshi · Roland Goecke · Gordon Barraclough Parker · Michael J Breakspear

123 citations0/0 files storedInternational Conference on Automatic Face and Gesture Recognition

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Emotion and Mood Recognition Face recognition and analysis Human Pose and Action Recognition

Abstract

Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis.

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References

2003The Epidemiology of Major Depressive Disorder 1997Affective Computing 1995Emotion elicitation using films 2005Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior 2012Affective Body Expression Perception and Recognition: A Survey 2009Detecting depression from facial actions and vocal prosody 2006Bi-modal emotion recognition from expressive face and body gestures 2007Recognising Human Emotions from Body Movement and Gesture Dynamics

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2018Integrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures 2018Automatic Depression Analysis Using Dynamic Facial Appearance Descriptor and Dirichlet Process Fisher Encoding 2017Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions 2023Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review 2015Multimodal Detection of Depression in Clinical Interviews

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