TY - GEN
T1 - InfAnFace
T2 - 26th International Conference on Pattern Recognition, ICPR 2022
AU - Wan, Michael
AU - Zhu, Shaotong
AU - Luan, Lingfei
AU - Prateek, Gulati
AU - Huang, Xiaofei
AU - Schwartz-Mette, Rebecca
AU - Hayes, Marie
AU - Zimmerman, Emily
AU - Ostadabbas, Sarah
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Facial landmark estimation
KW - computer vision
KW - domain adaptation
KW - prodromal risk screening
UR - https://www.scopus.com/pages/publications/85143611460
U2 - 10.1109/ICPR56361.2022.9956647
DO - 10.1109/ICPR56361.2022.9956647
M3 - Conference contribution
AN - SCOPUS:85143611460
T3 - Proceedings - International Conference on Pattern Recognition
SP - 4486
EP - 4492
BT - 2022 26th International Conference on Pattern Recognition, ICPR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 August 2022 through 25 August 2022
ER -