Automatic Red-Eye Removal

Authors

Flavien Volken, Johann Terrier, and Patrick Vandewalle

Abstract

It is well known that taking portrait photographs with a built in camera may create a red-eye effect. This effect is caused by the light entering the subject’s eye through the pupil and reflecting from the retina back to the sensor. These red eyes are one of the most important types of artifacts in portrait pictures. Many different techniques exist for removing these artifacts digitally after image capture. In most of the existing software tools, the user has to manually select the zone in which the red eye is located.

non-caucasian person with red eyes non-caucasian person with red eyes corrected

The aim of our method is to automatically detect and correct the red eyes. Our algorithm detects the eye itself by finding the appropriate colors and shapes without input from the user. We use the basic knowledge that an eye is characterized by its shape and the white color of the sclera. Combining this intuitive approach with the detection of “skin” around the eye, we obtain a higher success rate than most of the tools we tested. Moreover, our algorithm works for any type of skin tone. The main goal of this algorithm is to accurately remove red eyes from a picture, while avoiding false positives completely, which is the biggest problem of camera integrated algorithms or distributed software tools. At the same time, we want to keep the false negative rate as low as possible. We implemented this algorithm in a web-based application to allow people to correct their images online.

person with glasses person with glasses corrected

Collaborations

Sabine Süsstrunk

Publications

F. Volken, J. Terrier and P. Vandewalle, Automatic Red-Eye Removal based on Sclera and Skin Tone Detection, Proc. IS&T Third European Conference on Color in Graphics, Imaging and Vision (CGIV), pp. 359-364, 2006.
[detailed record] [bibtex]

Funding

National Competence Center in Research on Mobile Information and Communication Systems (NCCR-MICS), a center supported by the Swiss National Science Foundation under grant number 5005-67322.