Automated measurement of infant and mother Duchenne facial expressions in the Face-to-Face/Still-Face

Yeojin Amy Ahn, Itir Önal Ertuğrul, Sy Miin Chow, Jeffrey F. Cohn, Daniel S. Messinger

Research output: Contribution to journalArticlepeer-review


Although still-face effects are well-studied, little is known about the degree to which the Face-to-Face/Still-Face (FFSF) is associated with the production of intense affective displays. Duchenne smiling expresses more intense positive affect than non-Duchenne smiling, while Duchenne cry-faces express more intense negative affect than non-Duchenne cry-faces. Forty 4-month-old infants and their mothers completed the FFSF, and key affect-indexing facial Action Units (AUs) were coded by expert Facial Action Coding System coders for the first 30 s of each FFSF episode. Computer vision software, automated facial affect recognition (AFAR), identified AUs for the entire 2-min episodes. Expert coding and AFAR produced similar infant and mother Duchenne and non-Duchenne FFSF effects, highlighting the convergent validity of automated measurement. Substantive AFAR analyses indicated that both infant Duchenne and non-Duchenne smiling declined from the FF to the SF, but only Duchenne smiling increased from the SF to the RE. In similar fashion, the magnitude of mother Duchenne smiling changes over the FFSF were 2–4 times greater than non-Duchenne smiling changes. Duchenne expressions appear to be a sensitive index of intense infant and mother affective valence that are accessible to automated measurement and may be a target for future FFSF research.

Original languageEnglish (US)
Pages (from-to)910-929
Number of pages20
Issue number5
StatePublished - Sep 1 2023

All Science Journal Classification (ASJC) codes

  • Pediatrics, Perinatology, and Child Health
  • Developmental and Educational Psychology

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