InfAnFace: Bridging the Infant-Adult Domain Gap in Facial Landmark Estimation in the Wild

Michael Wan, Shaotong Zhu, Lingfei Luan, Gulati Prateek, Xiaofei Huang, Rebecca Schwartz-Mette, Marie Hayes, Emily Zimmerman, Sarah Ostadabbas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders. Specifically, we introduce the first-ever dataset of infant faces annotated with facial landmark coordinates and pose attributes, demonstrate the inadequacies of existing facial landmark estimation algorithms in the infant domain, and train new state-of-the-art models that significantly improve upon those algorithms using domain adaptation techniques.

Original languageEnglish (US)
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4486-4492
Number of pages7
ISBN (Electronic)9781665490627
DOIs
StatePublished - 2022
Externally publishedYes
Event26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: Aug 21 2022Aug 25 2022

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2022-August
ISSN (Print)1051-4651

Conference

Conference26th International Conference on Pattern Recognition, ICPR 2022
Country/TerritoryCanada
CityMontreal
Period8/21/228/25/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • computer vision
  • domain adaptation
  • Facial landmark estimation
  • prodromal risk screening

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