The DOI matches its Crossref record; 95 of the 99 references it cites were found in a registry or an index.
Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses
Open accessAbstract
The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units (AUs) and showed similar effects for change over time in depression severity. When symptom severity was high, participants made fewer affiliative facial expressions (AUs 12 and 15) and more non-affiliative facial expressions (AU 14). Participants also exhibited diminished head motion (i.e., amplitude and velocity) when symptom severity was high. These results are consistent with the Social Withdrawal hypothesis: that depressed individuals use nonverbal behavior to maintain or increase interpersonal distance. As individuals recover, they send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and revealed the same pattern of findings suggests that automatic facial expression analysis may be ready to relieve the burden of manual coding in behavioral and clinical science.
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“Depression recognition from FEs is a very important application in analysis of psychological distress [53], [54], [55].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [53]
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A review of depression and suicide risk assessment using speech analysis
Cummins, N., Scherer, S. J., Krajewski, J., Schnieder, S., Epps, J., & Quatieri, T. F. (2015) · Speech Communication · doi:10.1016/j.specom.2015.03.004 · Cited by 1,051
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database
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Visually Interpretable Representation Learning for Depression Recognition from Facial Images
Zhou, X., Jin, K., Shang, Y., & Guo, G. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2828819 · Cited by 278
Metadata
- Authors
- Girard, J. M., Cohn, J. F., Mahoor, M. H., Mavadati, S. M., Hammal, Z., & Rosenwald, D. P.
- Affiliations
- University of Pittsburgh (US)
- University of Denver (US)
- Carnegie Mellon University (US)
- Corresponding author
- Girard, Jeffrey M.
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 32
- Issue
- 10
- Pages
- 641-647
- ISSN
- 0262-8856 (ISSN-L)
- PMC
- PMC4217695
- OpenAlex
- W2096976598
- Cited by
- 255
- References
- 99
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 19 · 2023: 31 · 2022: 21 · 2021: 19 · 2020: 28 · 2019: 19 · 2018: 30 · 2017: 16 · 2016: 10 · 2015: 14 · 2014: 12
- FWCI
- 16.26
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Image and Vision Computing (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Commons - DU (University of Denver) (submittedVersion)
- Funders
- National Institutes of Health (MH61435, MH65376, MH096951)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- depression, nonverbal behavior, social withdrawal, facial expressions, head orientation, facial action units, manual coding, affiliative behavior, head movement, interpersonal distance, treatment response, video analysis
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