  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.
