Age classification from facial images for detecting retinoblastoma.

Facial age estimation from images is a difficult problem, both because it is naturally difficult to tell the exact age of a person visually, and because of the variations in images, such as illumination, pose, and expression. We want to classify people into two groups, children (age ≤ 5) and adults (age > 5), to facilitate the detection of retinoblastoma, a type of pediatric cancer. Current regression based methods are ineffective, as they usually have mean absolute error of 5 years, which is too high for our purposes. We study the facial anthropometric measurements of humans at different ages, and build a system based on these growth patterns. We detect 76 facial landmarks using Active Shape Models, analyze all possible ratios computable from these landmarks, and use the best ratios as input into a Support Vector Machine. Our final system does very well on our problem, correctly classifying 85% of images.