Quantifying dynamic facial expressions under naturalistic conditions

被引：8

作者：

Jeganathan, Jayson ^{[1
,2
]}

Campbell, Megan ^{[1
,2
]}

Hyett, Matthew ^{[3
]}

Parker, Gordon ^{[4
]}

Breakspear, Michael ^{[5
]}

机构：

[1] Univ Newcastle, Coll Engn Sci & Environm, Sch Psychol, Newcastle, Australia

[2] Hunter Med Res Inst, Newcastle, Australia

[3] Univ Western Australia, Sch Psychol Sci, Perth, Australia

[4] Univ New South Wales, Sch Psychiat, Kensington, Australia

[5] Univ Newcastle, Coll Med Hlth & Wellbeing, Sch Med & Publ Hlth, Newcastle, Australia

来源：

ELIFE | 2022年 / 11卷

基金：

澳大利亚研究理事会; 英国医学研究理事会;

关键词：

facial expression; major depressive disorder; naturalistic; Human; RECOGNITION; SEQUENCES;

D O I：

10.7554/eLife.79581

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Facial affect is expressed dynamically - a giggle, grimace, or an agitated frown. However, the characterisation of human affect has relied almost exclusively on static images. This approach cannot capture the nuances of human communication or support the naturalistic assessment of affective disorders. Using the latest in machine vision and systems modelling, we studied dynamic facial expressions of people viewing emotionally salient film clips. We found that the apparent complexity of dynamic facial expressions can be captured by a small number of simple spatiotemporal states - composites of distinct facial actions, each expressed with a unique spectral fingerprint. Sequential expression of these states is common across individuals viewing the same film stimuli but varies in those with the melancholic subtype of major depressive disorder. This approach provides a platform for translational research, capturing dynamic facial expressions under naturalistic conditions and enabling new quantitative tools for the study of affective disorders and related mental illnesses.

引用

页数：20

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