MFS is caused by a mutation in FBN1, one of the genes that makes fibrillin, which results in abnormal connective tissue. The severity of the symptoms of MFS is variable. The lungs, eyes, bones, and the covering of the spinal cord are also commonly affected. The most serious complications involve the heart and aorta, with an increased risk of mitral valve prolapse and aortic aneurysm. They also typically have overly-flexible joints and scoliosis. Those with the condition tend to be tall and thin, with long arms, legs, fingers, and toes. Marfan syndrome ( MFS) is a rare multi-systemic genetic disorder that affects the connective tissue. Loeys-Dietz syndrome, Ehlers-Danlos syndromeīeta blockers, calcium channel blockers, ACE inhibitors Scoliosis, mitral valve prolapse, aortic aneurysm Tall, thin build long arms, legs and fingers flexible fingers and toes The work - which was funded by Google and Intel - is to be presented at the IEEE Computer Vision and Pattern Recognition conference in June.Ectopia lentis in Marfan syndrome: Zonular fibers are seen. When shown images of an age-progressed child photo and a photo of the same person as an adult, people are unable to reliably identify which one is the real photo." "We've invented a method for "lighting-aware flow estimation" between such photos, and this opened up a huge amount of applications, the key in which is to use "big visual data" for novel face modelling and synthesis.Ĭo-author Steven Seitz said in a statement: "Our extensive user studies demonstrated age progression results that are so convincing that people can't distinguish them from reality. with unknown lighting, viewpoint and expression. Kemelmacher-Shlizerman told .uk that the biggest challenge was coming up with a method for "completely automatic analysis of face photos 'in the wild'", i.e. The study authors say that future improvements for the work include: modelling wrinkles and hair whitening to enhance the realism of older subjects, increasing the range of ethnicities, and having a database of heads and upper torsos of different ages in order to apply the same technique to. The results seemed to show that for ageing young children, the University of Washington's technique outperformed all prior work.
The team's results were also placed alongside the results from other ageing techniques, including Faceresearch PsychMorph and a technique developed by David Ian Perrett from the University of St Andrews. 37 percent (out of 8,916 votes) said that the University of Washington team's approach was more likely to be the older baby, 44 percent saying the actual image was more likely.ġ5 percent of people said that both were equally likely to be the adult version of the baby, while five percent said neither were likely. The results seem to show that humans identified the generated image as the older version almost as often as they identified the actual older image. The volunteers had to say which of the two older photos were more like the baby.
One picture would be an individual as a baby, and the two additional photos would be that person at a specific age (say 25) - one generated by the software and one actual image of that person at that age. This was put to the test by showing three pictures to human subjects (through Amazon's Mechanical Turk). This allowed them to see how effective the software was at accurately ageing the children. To check the efficacy of the system, the team fed in child images of individuals for whom they also had adolescent and adult images. It takes around 30 seconds to generate an older face using a standard PC.