Automated measurement of infant and mother Duchenne facial expressions in the Face‐to‐Face/Still‐Face
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 expr...
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| Published in | Infancy Vol. 28; no. 5; pp. 910 - 929 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Wiley Subscription Services, Inc
01.09.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1525-0008 1532-7078 1532-7078 |
| DOI | 10.1111/infa.12556 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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 (FACS) coders for the first 30 seconds of each FFSF episode. Computer vision software, automated facial affect recognition (AFAR), identified AUs for the entire 2-minute 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. 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.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. |
| Author | Messinger, Daniel S. Ahn, Yeojin Amy Cohn, Jeffrey F. Chow, Sy‐Miin Önal Ertuğrul, Itir |
| AuthorAffiliation | 1 Department of Psychology, University of Miami, Coral Gables, Florida, USA 6 Departments of Pediatrics and Music Engineering, University of Miami, Coral Gables, Florida, USA 3 Department of Human Development and Family Studies, Pennsylvania State University, State College, Pennsylvania, USA 2 Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands 4 Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA 5 Department of Electrical and Computer Engineering, University of Miami, Coral Gables, Florida, USA |
| AuthorAffiliation_xml | – name: 6 Departments of Pediatrics and Music Engineering, University of Miami, Coral Gables, Florida, USA – name: 5 Department of Electrical and Computer Engineering, University of Miami, Coral Gables, Florida, USA – name: 3 Department of Human Development and Family Studies, Pennsylvania State University, State College, Pennsylvania, USA – name: 2 Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands – name: 4 Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA – name: 1 Department of Psychology, University of Miami, Coral Gables, Florida, USA |
| Author_xml | – sequence: 1 givenname: Yeojin Amy orcidid: 0000-0003-3504-6909 surname: Ahn fullname: Ahn, Yeojin Amy organization: University of Miami – sequence: 2 givenname: Itir surname: Önal Ertuğrul fullname: Önal Ertuğrul, Itir organization: Utrecht University – sequence: 3 givenname: Sy‐Miin surname: Chow fullname: Chow, Sy‐Miin organization: Pennsylvania State University – sequence: 4 givenname: Jeffrey F. surname: Cohn fullname: Cohn, Jeffrey F. organization: University of Pittsburgh – sequence: 5 givenname: Daniel S. surname: Messinger fullname: Messinger, Daniel S. email: dmessinger@miami.edu organization: University of Miami |
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| Copyright | 2023 International Congress of Infant Studies. 2023. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Automation Coding Computer vision Facial Expression Female Humans Infant Mothers Mothers - psychology Smiling - psychology Software Validity Young Children |
| Title | Automated measurement of infant and mother Duchenne facial expressions in the Face‐to‐Face/Still‐Face |
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