A modular procedure for automatic red-eye correction in digital photos

The paper describes an algorithm for the automatic removal of "redeye" from digital photos. First an adaptive color cast removal algorithm is applied to correct the color photo. This phase not only facilitates the subsequent steps of processing, but also improves the overall appearance of the output image. A skin detector, based mainly on analysis of the chromatic distribution of the image, creates a probability map of skin-like regions. A multi-resolution neural network approach is then exploited to create an analogous probability map of candidate faces. These two distributions are then combined to identify the most probable facial regions in the image. Redeye is searched for within these regions, seeking areas with high "redness" and applying geometric constraints to limit the number of false hits. The redeye removal algorithm is then applied automatically to the red eyes identified. Candidate areas are opportunely smoothed to avoid unnatural transitions between the corrected and original parts of the eyes. Experimental results of application of this procedure on a set of over 300 images are presented.