Automated down syndrome detection using facial photographs

Qian Zhao, Kenneth Rosenbaum, Kazunori Okada, Dina J. Zand, Raymond Sze, Marshall Summar, Marius George Linguraru
2013 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation; its early detection is crucial. Children with Down syndrome generally have distinctive facial characteristics, which brings an opportunity for the computer-aided diagnosis of Down syndrome using photographs of patients. In this study, we propose a novel strategy based on machine learning techniques to detect Down syndrome automatically. A modified constrained local
more » ... model is used to locate facial landmarks. Then geometric features and texture features based on local binary patterns are extracted around each landmark. Finally, Down syndrome is detected using a variety of classifiers. The best performance achieved 94.6% accuracy, 93.3% precision and 95.5% recall by using support vector machine with radial basis function kernel. The results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way. 978-1-4577-0216-7/13/$26.00 ©2013 IEEE
doi:10.1109/embc.2013.6610339 pmid:24110526 dblp:conf/embc/ZhaoROZSSL13 fatcat:k7rawzjzx5dr7ftuxn3y24uxgq