InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild
This work addresses a domain gap in computer vision for healthcare applications like early prediction of developmental disorders, though it appears incremental as it adapts existing techniques to a new domain.
The researchers tackled the problem of facial landmark estimation for infant faces, which existing algorithms perform poorly on, by creating the first annotated infant face dataset and developing domain adaptation models that significantly improved performance.
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. We touch on the closely related task of facial detection for infants, and also on a challenging case study of infrared baby monitor images gathered by our lab as part of in-field research into the aforementioned developmental issues.