Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2010, Article ID 909043, 11 pages doi:10.1155/2010/909043 Research Article Red-Eyes Removal through Cluster-Based Boosting on Gray Codes Sebastiano Battiato, 1 Giovanni Maria Farinella, 1 Mirko Guarnera, 2 Giuseppe Messina, 2 and Daniele Rav` ı 1 1 Image Processing Laboratory, Dipartimento di Matematica e Informatica, Universit` a di Catania, Viale A. Doria 6, 95125 Catania, Italy 2 Advanced System Technology, STMicroelectronics, Stradale Primosole 50, 95125 Catania, Italy Correspondence should be addressed to Giovanni Maria Farinella, gfarinella@dmi.unict.it Received 26 March 2010; Revised 2 July 2010; Accepted 29 July 2010 Academic Editor: Lei Zhang Copyright © 2010 Sebastiano Battiato et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the redeyes artifacts have de facto become a critical problem. The technique herein described makes use of three main steps to identify and remove red eyes. First, red-eye candidates are extracted from the input image by using an image filtering pipeline. A set of classifiers is then learned on gray code features extracted in the clustered patches space and hence employed to distinguish between eyes and non-eyes patches. Specifically, for each cluster the gray code of the red-eyes candidate is computed and some discriminative gray code bits are selected employing a boosting approach. The selected gray code bits are used during the classification to discriminate between eye versus non-eye patches. Once red-eyes are detected, artifacts are removed through desaturation and brightness reduction. Experimental results on a large dataset of images demonstrate the effectiveness of the proposed pipeline that outperforms other existing solutions in terms of hit rates maximization, false positives reduction, and quality measure. 1. Introduction Red-eye artifact is caused by the flash light reflected off a person’s retina (see Figure 1). 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 capturing. Rapid preflashes cause pupil contraction, thus, minimizing the area of reflection; it does not completely eliminate the red-eye effect though it reduces it. The major disadvantage of the preflash approach is power consumption (e.g., flash is the most power- consuming device of the camera). Besides, repeated flashes usually cause uncomfortable feeling. Alternatively, red eyes can be detected after photo acqui- sition. Some photo-editing software makes use of red-eye removal tools which require considerable user interaction. To overcome this problem, different techniques have been proposed in literature (see [1, 2] for recent reviews in the field). Due to the growing interest of industry, many automatic algorithms, embedded on commercial software, have been patented in the last decade [3]. The huge variety of approaches has permitted to explore different aspects of red- eyes identification and correction. The big challenge now is to obtain the best results with the minor number of visual errors. In this paper, an advanced pipeline for red-eyes detection and correction is discussed. In the first stage, candidates rede- yes patches are extracted from the input image through an image filtering pipeline. This process is mainly based on a statistical color model technique coupled with geometrical constraints. In the second stage, a multimodal classifier, obtained by using clustering and boosting on gray codes features, is used to distinguish between true red-eyes patches versus other patches. Once the red eyes are detected, a correction technique based on desaturation and brightness reduction is employed to remove the red-eyes artifact. The proposed approach has been compared with respect to existing solutions on proper collected dataset, obtaining competitive results. One of the main contributions of the