A Cluster-Based Boosting Strategy for Red Eye Removal

Abstract

Red eye artifact is caused by the flash light reflected off a person’s retina. This effect often occurs when the flash light is very close to the camera lens, as in most compact imaging devices. To reduce these artifacts, most cameras have a red eye flash mode which fires a series of preflashes prior to picture capture. The major disadvantage of the preflash approach is power consumption (e.g., flash is the most power-consuming device on the camera). Alternatively, red eyes can be detected after photo acquisition. Some photo-editing softwares make use of red eye removal tools that require considerable user interaction. To overcome this problem, different techniques have been proposed in literature. Due to the growing interest of industry, many automatic algorithms, embedded on commercial software, have been patented in the last decade. The huge variety of approaches has permitted research to explore different aspects and problems of red eyes identification and correction. The big challenge now is to obtain the best results with the minimal number of visual errors. This chapter critically reviews some of the state-of-the-art approaches for red eye removal. We also discuss a recent technique whose strength is due to a multimodal classifier which is obtained by combining clustering and boosting in order to recognize red eyes represented in the gray codes feature space.