Abstract
Abstract Down syndrome, besides from being identified by chromosome tests, is mainly diagnosed by experts according to the subject’s appearance. In this article, 3D models are built from single frontal face images; then, features are extracted and selected automatically to train a classifier used to identify Down syndrome from images. Down syndrome, or trisomy 21, is a chromosomal disorder caused by the presence of an extra 21st chromosome. The incidence of Down syndrome is estimated at 1 per 800 births. The common physical features of Down syndrome include abnormally shaped facial features, flat facial profile, upward, outward slanting palpebral fissures, small nose, ear, chin, a protruding tongue, and abnormal facial fat distribution. The purpose of this system is to automatically identify Down syndrome from a set of frontal images, without the need of an expert. Time and labor are thus saved. In this experiment, features are first extracted from frontal face images using active appearance models. Then, an initial 3D model is obtained by fitting these sparse feature points to a 3DMM, with descriptive shape parameter terms being fitted first. Since individuals with Down syndrome usually have a flat facial profile, a PCAmodel coupling frontal image intensity difference with depth is trained. This model is used to adjust the initial model. Finally, geometric features are extracted from the 3Dmodel. Together with shape parameters, they are feature-selected and sent to a SVM classifier for training. The experimental results show that the accuracy is above 90%.