Red eye detection with machine learning

Red-eye is a problem in photography that occurs when a photograph is taken with a flash, and the bright flash light is reflected from the blood vessels in the eye, giving the eye an unnatural red hue. Most red-eye reduction systems need the user to outline the red eyes by hand, but this approach doesn´t scale up. Instead, we propose an automatic red-eye detection system. The system contains a red-eye detector that finds red eye-like candidate image patches; a state of the art face detector used to eliminate most false positives (image regions that look but red eyes but are not); and a red-eye outline detector. All three detectors are automatically learned from data, using Boosting. Our system can be combined with a red-eye reduction module to yield a fully automatic red eye corrector.