  Computer vision-based methods have valuable use cases in precision medicine,
and recognizing facial phenotypes of genetic disorders is one of them. Many
genetic disorders are known to affect faces' visual appearance and geometry.
Automated classification and similarity retrieval aid physicians in
decision-making to diagnose possible genetic conditions as early as possible.
Previous work has addressed the problem as a classification problem and used
deep learning methods. The challenging issue in practice is the sparse label
distribution and huge class imbalances across categories. Furthermore, most
disorders have few labeled samples in training sets, making representation
learning and generalization essential to acquiring a reliable feature
descriptor. In this study, we used a facial recognition model trained on a
large corpus of healthy individuals as a pre-task and transferred it to facial
phenotype recognition. Furthermore, we created simple baselines of few-shot
meta-learning methods to improve our base feature descriptor. Our quantitative
results on GestaltMatcher Database show that our CNN baseline surpasses
previous works, including GestaltMatcher, and few-shot meta-learning strategies
improve retrieval performance in frequent and rare classes.
