Infant AFAR: Automated facial action recognition in infants.

Automatic facial action unit detection Cross domain generalizability Deep learning Facial action coding system Infant behavior

Journal

Behavior research methods
ISSN: 1554-3528
Titre abrégé: Behav Res Methods
Pays: United States
ID NLM: 101244316

Informations de publication

Date de publication:
04 2023
Historique:
accepted: 13 04 2022
medline: 26 4 2023
pubmed: 11 5 2022
entrez: 10 5 2022
Statut: ppublish

Résumé

Automated detection of facial action units in infants is challenging. Infant faces have different proportions, less texture, fewer wrinkles and furrows, and unique facial actions relative to adults. For these and related reasons, action unit (AU) detectors that are trained on adult faces may generalize poorly to infant faces. To train and test AU detectors for infant faces, we trained convolutional neural networks (CNN) in adult video databases and fine-tuned these networks in two large, manually annotated, infant video databases that differ in context, head pose, illumination, video resolution, and infant age. AUs were those central to expression of positive and negative emotion. AU detectors trained in infants greatly outperformed ones trained previously in adults. Training AU detectors across infant databases afforded greater robustness to between-database differences than did training database specific AU detectors and outperformed previous state-of-the-art in infant AU detection. The resulting AU detection system, which we refer to as Infant AFAR (Automated Facial Action Recognition), is available to the research community for further testing and applications in infant emotion, social interaction, and related topics.

Identifiants

pubmed: 35538295
doi: 10.3758/s13428-022-01863-y
pii: 10.3758/s13428-022-01863-y
pmc: PMC9646921
mid: NIHMS1826008
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1024-1035

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH096951
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002319
Pays : United States

Informations de copyright

© 2022. The Author(s).

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Auteurs

Itir Onal Ertugrul (I)

Utrecht University, Utrecht, The Netherlands. i.onalertugrul@uu.nl.

Yeojin Amy Ahn (YA)

University of Miami, Miami, FL, USA.

Maneesh Bilalpur (M)

University of Pittsburgh, Pittsburgh, PA, USA.

Daniel S Messinger (DS)

University of Miami, Miami, FL, USA.

Matthew L Speltz (ML)

University of Washington, Seattle, WA, USA.

Jeffrey F Cohn (JF)

University of Pittsburgh, Pittsburgh, PA, USA.

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Classifications MeSH