A Review of Redeye Detection and Removal in Digital Images Through Patents
---
paper_title: Human Face Detection in Visual Scenes

paper_content:

We present a neural network-based face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images. Comparisons with another state-of-the-art face detection system are presented; our system has better performance in terms of detection and false-positive rates.
---
paper_title: Automatic Red-Eye Removal based on Sclera and Skin Tone Detection

paper_content:

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 probably 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 select the zone in which the red eye is located. 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 haracterized 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.
---
paper_title: Red Eye Removal using Digital Color Image Processing

paper_content:

The current paper provides methods to correct the artifact known as “red eye” by means of digital color image processing. This artifact is typically formed in amateur photographs taken with a built-in camera flash. To correct red eye artifacts, an image mask is computed by calculating a colorimetric distance between a prototypical reference “red eye” color and each pixel of the image containing the red eye. Various image processing algorithms such as thresholding, blob analysis, and morphological filtering, are applied to the mask, in order to eliminate noise, reduce errors, and facilitate a more natural looking result. The mask serves to identify pixels in the color image needing correction, and further serves to identify the amount of correction needed. Pixels identified as having red-eye artifacts are modified to a substantially monochrome color, while the bright specular reflection of the eye is preserved.
---
paper_title: An efficient automatic redeye detection and correction algorithm

paper_content:

A fully automatic redeye detection and correction algorithm is presented to address the redeye artifacts in digital photos. The algorithm contains a redeye detection part and a correction part. The detection part is modeled as a feature based object detection problem. Adaboost is used to simultaneously select features and train the classifier. A new feature set is designed to address the orientation-dependency problem associated with the Haar-like features commonly used for object detection design. For each detected redeye, a correction algorithm is applied to do adaptive desaturation and darkening over the redeye region.
---
paper_title: Red eye detection with machine learning

paper_content:

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.
---
paper_title: Probabilistic Automatic Red Eye Detection and Correction

paper_content:

In this paper we propose a new probabilistic approach to red eye detection and correction. It is based on stepwise refinement of a pixel-wise red eye probability map. Red eye detection starts with a fast non red eye region rejection step. A classification step then adjusts the probabilities attributed to the detected red eye candidates. The correction step finally applies a soft red eye correction based on the resulting probability map. The proposed approach is fast and allows achieving an excellent correction of strong red eyes while producing a still significant correction of weaker red eyes.
---
paper_title: Automatic red-eye detection and correction

paper_content:

"Red-eye" is a phenomenon that causes the eyes of flash photography subjects to appear unnaturally reddish in color. Though commercial solutions exist for red-eye correction, all of them require some measure of user intervention. A method is presented to automatically detect and correct redeye in digital images. First, faces are detected with a cascade of multi-scale classifiers. The red-eye pixels are then located with several refining masks computed over the facial region. The masks are created by thresholding per-pixel metrics, designed to detect red-eye artifacts. Once the redeye pixels have been found, the redness is attenuated with a tapered color desaturation. A detector implemented with this system corrected 95% of the red-eye artifacts in 200 tested images.
---
paper_title: Robust real-time object detection

paper_content:

This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. Author email: fPaul.Viola,Mike.J.Jonesg@compaq.com c Compaq Computer Corporation, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research.compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA
---
paper_title: A Brief Introduction to Boosting

paper_content:

Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting. Some examples of recent applications of boosting are also described.
---
paper_title: Automatic red-eyes detection based on AAM

paper_content:

Red-eye is a phenomenon that the pupils of people appear unnaturally red when an image is captured using photoflash lamp. There are some algorithms existing for red-eyes detection, but all of them have less accuracy and cannot detect single red-eye. In this paper, a novel approach is proposed to automatically detect red eyes in photos, which is based on active appearance models, and a practicable automatic correction method is presented The goal of the algorithm is to detect all the red-eyes in photos without any human intervention. Finally, experimental results demonstrate the high accuracy and efficiency of the method.
---
paper_title: Towards automatic redeye effect removal

paper_content:

The redeye effect is typically formed in amateur photographs taken with a built-in camera flash. Analysis of the available techniques and products indicates that their efficiency in correcting this artifact is limited and their performance is inconsistent. In this work we propose a user friendly solution, which could be used to restore amateur photographs. In the proposed method the redeye effect is detected using a skin detection module and eye colors are restored using morphological image processing. The new method is computationally efficient, robust to parameter settings and versatile, as it can work in conjunction with a number of skin detection methods.
---
paper_title: Digital red eye removal

paper_content:

The current paper provides methods to correct the artifact known as red eye by means of digital color image processing. This artifact is typically formed in amateur photographs taken with a built-in camera flash. To correct red eye artifacts, an image mask is computed by calculating a colorimetric distance between a prototypical reference red eye color and each pixel of the image containing the red eye. Various image processing algorithms such as thresholding, blob analysis, and morphological filtering, are applied to the mask, in order to eliminate noise, reduce errors, and facilitate a more natural looking result. The mask serves to identify pixels in the color image needing correction, and further serves to identify the amount of correction needed. Pixels identified as having red eye artifacts are modified to a substantially monochrome color, while the bright specular reflection of the eye is preserved.
---
paper_title: A fully automatic redeye detection and correction algorithm

paper_content:

A fully automatic redeye detection and correction algorithm was developed at Eastman Kodak Company Research Laboratories. The algorithm is highly sophisticated so that it is able to distinguish most redeye pairs from scene content. It is also highly optimized for execution speed and memory usage enabling it to be included in a variety of products. Detected redeyes are corrected so that the red color is removed, but the eye maintains a natural look.
---
paper_title: Automatic redeye removal for smart enhancement of photos of unknown origin

paper_content:

The paper describes a modular procedure for automatic correction of redeye artifact in images of unknown origin, maintaining the natural appearance of the eye. First, a smart color balancing procedure is applied. This phase not only facilitates the subsequent steps of processing, but also improves the overall appearance of the output image. Combining the results of a color-based face detector and of a face detector based on a multi-resolution neural network the most likely facial regions are identified. Redeye is searched for only within these regions, seeking areas with high “redness” satisfying some geometric constraints. A novel redeye removal algorithm is then applied automatically to the red eyes identified, and opportunely smoothed to avoid unnatural transitions between the corrected and original parts. Experimental results on a set of over 450 images are reported.
---
paper_title: Safe Red-Eye Correction Plug-in Using Adaptive Methods

paper_content:

An important issue with red-eye correction is that it might result in image degradation. This can be due to the detection of false positives or, even in the case of correct detection, to an inadequate correction technique. Three correction methods are proposed and compared according to their image degradation risk and their expected perceptual quality improvement. Based on those analyses an adaptive system is designed which selects the correction strategy dependent on those measures and the detection confidence. Finally, both qualitative (visual preferences) and quantitative (pixel counts on manual segmented images) evaluation results are shown.
---
paper_title: Automatic Red-Eye Removal based on Sclera and Skin Tone Detection

paper_content:

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 probably 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 select the zone in which the red eye is located. 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 haracterized 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.
---
paper_title: Red eye detection with machine learning

paper_content:

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.
---
paper_title: Automatic red-eye detection and correction

paper_content:

"Red-eye" is a phenomenon that causes the eyes of flash photography subjects to appear unnaturally reddish in color. Though commercial solutions exist for red-eye correction, all of them require some measure of user intervention. A method is presented to automatically detect and correct redeye in digital images. First, faces are detected with a cascade of multi-scale classifiers. The red-eye pixels are then located with several refining masks computed over the facial region. The masks are created by thresholding per-pixel metrics, designed to detect red-eye artifacts. Once the redeye pixels have been found, the redness is attenuated with a tapered color desaturation. A detector implemented with this system corrected 95% of the red-eye artifacts in 200 tested images.
---
paper_title: Robust real-time object detection

paper_content:

This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. Author email: fPaul.Viola,Mike.J.Jonesg@compaq.com c Compaq Computer Corporation, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research.compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA
---
paper_title: Automatic red-eyes detection based on AAM

paper_content:

Red-eye is a phenomenon that the pupils of people appear unnaturally red when an image is captured using photoflash lamp. There are some algorithms existing for red-eyes detection, but all of them have less accuracy and cannot detect single red-eye. In this paper, a novel approach is proposed to automatically detect red eyes in photos, which is based on active appearance models, and a practicable automatic correction method is presented The goal of the algorithm is to detect all the red-eyes in photos without any human intervention. Finally, experimental results demonstrate the high accuracy and efficiency of the method.
---
paper_title: A fully automatic redeye detection and correction algorithm

paper_content:

A fully automatic redeye detection and correction algorithm was developed at Eastman Kodak Company Research Laboratories. The algorithm is highly sophisticated so that it is able to distinguish most redeye pairs from scene content. It is also highly optimized for execution speed and memory usage enabling it to be included in a variety of products. Detected redeyes are corrected so that the red color is removed, but the eye maintains a natural look.
---
paper_title: Automatic redeye removal for smart enhancement of photos of unknown origin

paper_content:

The paper describes a modular procedure for automatic correction of redeye artifact in images of unknown origin, maintaining the natural appearance of the eye. First, a smart color balancing procedure is applied. This phase not only facilitates the subsequent steps of processing, but also improves the overall appearance of the output image. Combining the results of a color-based face detector and of a face detector based on a multi-resolution neural network the most likely facial regions are identified. Redeye is searched for only within these regions, seeking areas with high “redness” satisfying some geometric constraints. A novel redeye removal algorithm is then applied automatically to the red eyes identified, and opportunely smoothed to avoid unnatural transitions between the corrected and original parts. Experimental results on a set of over 450 images are reported.
---
paper_title: Automated red-eye detection and correction in digital photographs

paper_content:

Caused by light reflected off the subject's retina, red-eye is a troublesome problem in consumer photography. Although most of the cameras have the red-eye reduction mode, the result reality is that no on-camera system is completely effective. In this paper, we propose a fully automatic approach to detecting and correcting red-eyes in digital images. In order to detect red-eyes in a picture, a heuristic yet efficient algorithm is first adopted to detect a group of candidate red regions and then an eye classifier is utilized to confirm whether each candidate region is a human eye. Thereafter, for each detected redeye, we can correct it by the correction algorithm. In case that a red-eye cannot be detected automatically, another algorithm is also provided to detect red-eyes manually with the user's interaction by clicking on an eye. Experimental results on about 300 images with various red-eye appearances demonstrate that the proposed solution is robust and effective.
---
paper_title: Automatic red-eye detection and correction

paper_content:

"Red-eye" is a phenomenon that causes the eyes of flash photography subjects to appear unnaturally reddish in color. Though commercial solutions exist for red-eye correction, all of them require some measure of user intervention. A method is presented to automatically detect and correct redeye in digital images. First, faces are detected with a cascade of multi-scale classifiers. The red-eye pixels are then located with several refining masks computed over the facial region. The masks are created by thresholding per-pixel metrics, designed to detect red-eye artifacts. Once the redeye pixels have been found, the redness is attenuated with a tapered color desaturation. A detector implemented with this system corrected 95% of the red-eye artifacts in 200 tested images.
---
paper_title: Towards automatic redeye effect removal

paper_content:

The redeye effect is typically formed in amateur photographs taken with a built-in camera flash. Analysis of the available techniques and products indicates that their efficiency in correcting this artifact is limited and their performance is inconsistent. In this work we propose a user friendly solution, which could be used to restore amateur photographs. In the proposed method the redeye effect is detected using a skin detection module and eye colors are restored using morphological image processing. The new method is computationally efficient, robust to parameter settings and versatile, as it can work in conjunction with a number of skin detection methods.
---
paper_title: Towards automatic redeye effect removal

paper_content:

The redeye effect is typically formed in amateur photographs taken with a built-in camera flash. Analysis of the available techniques and products indicates that their efficiency in correcting this artifact is limited and their performance is inconsistent. In this work we propose a user friendly solution, which could be used to restore amateur photographs. In the proposed method the redeye effect is detected using a skin detection module and eye colors are restored using morphological image processing. The new method is computationally efficient, robust to parameter settings and versatile, as it can work in conjunction with a number of skin detection methods.
---
paper_title: Automatic redeye removal for smart enhancement of photos of unknown origin

paper_content:

The paper describes a modular procedure for automatic correction of redeye artifact in images of unknown origin, maintaining the natural appearance of the eye. First, a smart color balancing procedure is applied. This phase not only facilitates the subsequent steps of processing, but also improves the overall appearance of the output image. Combining the results of a color-based face detector and of a face detector based on a multi-resolution neural network the most likely facial regions are identified. Redeye is searched for only within these regions, seeking areas with high “redness” satisfying some geometric constraints. A novel redeye removal algorithm is then applied automatically to the red eyes identified, and opportunely smoothed to avoid unnatural transitions between the corrected and original parts. Experimental results on a set of over 450 images are reported.
---
paper_title: Automatic Red-Eye Removal based on Sclera and Skin Tone Detection

paper_content:

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 probably 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 select the zone in which the red eye is located. 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 haracterized 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.
---
paper_title: Automatic red-eye detection and correction

paper_content:

"Red-eye" is a phenomenon that causes the eyes of flash photography subjects to appear unnaturally reddish in color. Though commercial solutions exist for red-eye correction, all of them require some measure of user intervention. A method is presented to automatically detect and correct redeye in digital images. First, faces are detected with a cascade of multi-scale classifiers. The red-eye pixels are then located with several refining masks computed over the facial region. The masks are created by thresholding per-pixel metrics, designed to detect red-eye artifacts. Once the redeye pixels have been found, the redness is attenuated with a tapered color desaturation. A detector implemented with this system corrected 95% of the red-eye artifacts in 200 tested images.
---
paper_title: A Brief Introduction to Boosting

paper_content:

Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting. Some examples of recent applications of boosting are also described.
---
paper_title: A fully automatic redeye detection and correction algorithm

paper_content:

A fully automatic redeye detection and correction algorithm was developed at Eastman Kodak Company Research Laboratories. The algorithm is highly sophisticated so that it is able to distinguish most redeye pairs from scene content. It is also highly optimized for execution speed and memory usage enabling it to be included in a variety of products. Detected redeyes are corrected so that the red color is removed, but the eye maintains a natural look.
---
paper_title: Safe Red-Eye Correction Plug-in Using Adaptive Methods

paper_content:

An important issue with red-eye correction is that it might result in image degradation. This can be due to the detection of false positives or, even in the case of correct detection, to an inadequate correction technique. Three correction methods are proposed and compared according to their image degradation risk and their expected perceptual quality improvement. Based on those analyses an adaptive system is designed which selects the correction strategy dependent on those measures and the detection confidence. Finally, both qualitative (visual preferences) and quantitative (pixel counts on manual segmented images) evaluation results are shown.
---
paper_title: Automated red-eye detection and correction in digital photographs

paper_content:

Caused by light reflected off the subject's retina, red-eye is a troublesome problem in consumer photography. Although most of the cameras have the red-eye reduction mode, the result reality is that no on-camera system is completely effective. In this paper, we propose a fully automatic approach to detecting and correcting red-eyes in digital images. In order to detect red-eyes in a picture, a heuristic yet efficient algorithm is first adopted to detect a group of candidate red regions and then an eye classifier is utilized to confirm whether each candidate region is a human eye. Thereafter, for each detected redeye, we can correct it by the correction algorithm. In case that a red-eye cannot be detected automatically, another algorithm is also provided to detect red-eyes manually with the user's interaction by clicking on an eye. Experimental results on about 300 images with various red-eye appearances demonstrate that the proposed solution is robust and effective.
---
paper_title: Automatic red-eye detection and correction

paper_content:

"Red-eye" is a phenomenon that causes the eyes of flash photography subjects to appear unnaturally reddish in color. Though commercial solutions exist for red-eye correction, all of them require some measure of user intervention. A method is presented to automatically detect and correct redeye in digital images. First, faces are detected with a cascade of multi-scale classifiers. The red-eye pixels are then located with several refining masks computed over the facial region. The masks are created by thresholding per-pixel metrics, designed to detect red-eye artifacts. Once the redeye pixels have been found, the redness is attenuated with a tapered color desaturation. A detector implemented with this system corrected 95% of the red-eye artifacts in 200 tested images.
---
paper_title: Towards automatic redeye effect removal

paper_content:

The redeye effect is typically formed in amateur photographs taken with a built-in camera flash. Analysis of the available techniques and products indicates that their efficiency in correcting this artifact is limited and their performance is inconsistent. In this work we propose a user friendly solution, which could be used to restore amateur photographs. In the proposed method the redeye effect is detected using a skin detection module and eye colors are restored using morphological image processing. The new method is computationally efficient, robust to parameter settings and versatile, as it can work in conjunction with a number of skin detection methods.
---
paper_title: Automatic redeye removal for smart enhancement of photos of unknown origin

paper_content:

The paper describes a modular procedure for automatic correction of redeye artifact in images of unknown origin, maintaining the natural appearance of the eye. First, a smart color balancing procedure is applied. This phase not only facilitates the subsequent steps of processing, but also improves the overall appearance of the output image. Combining the results of a color-based face detector and of a face detector based on a multi-resolution neural network the most likely facial regions are identified. Redeye is searched for only within these regions, seeking areas with high “redness” satisfying some geometric constraints. A novel redeye removal algorithm is then applied automatically to the red eyes identified, and opportunely smoothed to avoid unnatural transitions between the corrected and original parts. Experimental results on a set of over 450 images are reported.
---
paper_title: Safe Red-Eye Correction Plug-in Using Adaptive Methods

paper_content:

An important issue with red-eye correction is that it might result in image degradation. This can be due to the detection of false positives or, even in the case of correct detection, to an inadequate correction technique. Three correction methods are proposed and compared according to their image degradation risk and their expected perceptual quality improvement. Based on those analyses an adaptive system is designed which selects the correction strategy dependent on those measures and the detection confidence. Finally, both qualitative (visual preferences) and quantitative (pixel counts on manual segmented images) evaluation results are shown.
---

Title: A Review of Redeye Detection and Removal in Digital Images Through Patents

Section 1: INTRODUCTION
Description 1: Provide an introduction to the issue of redeye in photography, discussing its causes, frequency, and impact. Mention the importance of digital solutions for redeye removal and give an overview of the paper's structure.

Section 2: CHRONOLOGICAL REVIEW
Description 2: Present a timeline and review of patents related to redeye detection and removal. Discuss contributions and methods from major companies and highlight key developments in the technology.

Section 3: REDEYE DETECTION
Description 3: Describe the various strategies for detecting redeye in images. Differentiate between methods that reduce the search space and those that scan the entire image. Provide examples and explanations of each approach.

Section 4: RED EYE DETECTION MODULES
Description 4: Detail the components and processes involved in redeye detection modules, including color space conversion, segmentation, and pixel classification. Explain the importance of redness maps and different methodologies adopted to identify redeye pixels.

Section 5: REDEYE VALIDATION PROCESSES
Description 5: Outline the techniques used to validate detected redeye regions. Discuss criteria such as geometric constraints, the presence of sclera and glint, and skin tone analysis. Explain the steps taken to confirm candidate redeye areas.

Section 6: REDEYE CORRECTION
Description 6: Discuss the techniques and considerations for correcting redeye once detected. Explain different strategies for color correction, maintaining natural appearance, and avoiding abrupt variations. Include examples of various correction methods and their effectiveness.

Section 7: EVALUATION OF THE REDEYE DETECTION AND CORRECTION PROCESSES
Description 7: Describe how to evaluate the performance of redeye detection and correction processes. Include criteria and metrics used for assessment, and discuss common errors and their impacts on image quality.

Section 8: CURRENT & FUTURE DEVELOPMENTS
Description 8: Summarize the advancements in the redeye detection and removal field. Highlight areas needing further research and development, and discuss potential new challenges like peteye color correction. Suggest the creation of a reference dataset for consistent evaluation.