This document presents an automatic method for detecting and correcting red-eye in digital images. It begins with an overview of red-eye effect and existing solutions, then describes the proposed approach which uses iris segmentation. The approach has two main parts: red-eye detection using skin segmentation, edge detection, iris tracking and confirmation; and red-eye correction by reconstructing the red component. The method achieved 92% accuracy on a test set of 50 images and offers improvements over existing solutions such as being simple, fast and not requiring face detection. Future work could expand it to detect red-eye in animals.

1. AN AUTOMATIC METHOD FOR RED-EYE
DETECTION AND CORRECTION IN DIGITAL
IMAGES
Presented By
Md. Mushfekur Rahman (0513)
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2. OVERVIEW
○ What Is Red-Eye Effect?
○ Existing Solutions
○ Drawbacks
○ Our Approach
▪ Red-Eye Detection
▪ Red-Eye Correction
○ Results & Performance Analysis
○ Future Enhancements
○ References
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3. THE RED-EYE EFFECT
○ The Red-Eye artifact of digital
images is a common problem
happens when taking photos in
a flash-lighted environment
○ The pupil (sometimes the
whole iris) appears red instead
of its natural color
Fig. The Red-Eye Problem 3

4. EXISTING SOLUTIONS
○ An Efficient Automatic Redeye Detection and Correction
Algorithm [1]
● Proposed by Hewlett-Packard Labs
● Uses feature based object detection algorithm to detect red-
eyes
○ Some other solutions use statistical learning based
approaches [2, 3] and neural network based learning
algorithms [4]
● Based on machine learning algorithms
● Uses face/eye detection algorithms
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5. DRAWBACKS
○ Though they are widely accepted and currently being used in many
devices, they do not ensure 100% accuracy
○ Most of these solutions use machine learning which leads to higher
computations complexity
○ Most of them uses face and/or eye detection methods which
themselves are very complicated and considered as open research
problems
○ Some of the solutions (e.g. Gimp) require user intervention to point
out the red-eye region which is unpleasant and sometimes quite
tedious (what if you have hundreds of old photographs with red-eye
effect?)
For these reasons, an automatic red-eye detection and
correction system which is lightweight and fast yet
ensures higher accuracy rate is required and can make
life a lot easier 5

6. OUR APPROACH
○ To overcome these limitations we propose a new
solution based on iris segmentation
○ Our system contains two major sections:
▪ Red-Eye Detection and
▪ Red-Eye Correction
○ The detection phase is more challenging and consists of
four major steps
○ After determining the location and size of the red-eyes, a
correction algorithm is applied to each of them.
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7. RED-EYE DETECTION
○ The detection phase contains four modules:
▪ Skin-Based Segmentation
▪ Edge Detection
▪ Iris Tracking and
▪ Red-Eye Confirmation
Skin-Based
Segmentation
Edge
Detection
Iris Tracking
Red-Eye
Confirmation
Fig. Illustration of the detection phase
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8. SKIN BASED SEGMENTATION
○ The skin-based segmentation
● Reduces search space
● Extracts possible facial regions
○ We used YCbCr color model for skin segmentation
because-
● Lighting condition invariant
● RGB to YCbCr conversion is linear
○ Skin regions that cannot be a face are discarded using
some trivial checks (e.g. height-width ratio, presence of
hole etc.)
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9. SKIN BASED SEGMENTATION (CONTD.)
Skin
Segmentation
Fig. Result of Skin-Based Segmentation
(a) Original Image (b) Skin Segmented Image
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10. RED-EYE DETECTION (CONTD.)
○ Edge Detection
● Extracts edges and removes all other unnecessary
information from the image
● Required for iris tracking using circular Hough transform
○ We used Canny edge detector
● Very robust and the best edge detector so far
Edge Tracking
by Hysteresis
Gaussian
Smoothin
g
Sobel
Filtering
Non-
Maximum
Suppression
Double
Thresholding
Fig. Steps in Canny Edge Detector 10

11. CANNY EDGE DETECTOR
○ Gaussian Smoothing
● Removes noise from image using a 5x5 filtering mask
○ Sobel Filtering
● Simplest edge detector
● Uses discrete differentiation operator to calculate gradient of
the image function
○ Non-maximum Suppression
● Helps to find edges that are only one pixel wide
○ Double Thresholding
● Removes edges with lower gradient magnitude
○ Edge Tracking by Hysteresis
● Ensures that the detected edges are continuous real edges
(i.e. not noise)
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12. CANNY EDGE DETECTOR (CONTD.)
(a) Original Image (b) Edge Detected Image
Fig. Result of Canny Edge Detection
Canny Edge
Detector
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13. IRIS TRACKING
○ Since irises are the only circular object in human face, we
used circular Hough transform for iris localization
○ Circular Hough transform gives better performance for
iris tracking because
● Very robust algorithm (i.e. can even detect semi-circles)
● Runs quite fast
○ Circular Hough transform consists of the following two
steps-
● Accumulation into (a, b)-space
● Accumulation into r-space
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14. HOUGH TRANSFORM
○ Accumulation into (a, b)-space
● Transforms each of the points of (x, y)-plane to a circle in a
parametric space called (a, b)-space or accumulator space
● Finds the center of the circle
Fig. Accumulation into (a, b)-space
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15. HOUGH TRANSFORM (CONTD.)
○ Accumulation into r-space
● Finds the radius of the circle
● A search for finding maximum edge strength is conducted
using a predefined radius range (minr, maxr)
Fig. Accumulation into r-space
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16. IRIS TRACKING (CONTD.)
○ Thresholding (a, b)-space
● Due to exhaustive search, Hough transform may produce so
many unwanted circles
● A thresholding method is needed for extracting only iris
circles
● Visualizing (a, b)-space shows that, the perfect circles have
highest peak values
● For this reason we will only consider only four highest peak
values and discard the rest of them
● We are considering four of them to be on the safe side
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17. RED-EYE CONFIRMATION
○ Iris Confirmation
● A circle will be considered as an iris if it falls into one of the
holes in a skin region that we marked during the skin
segmentation
Fig. Result of Iris Tracking
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18. RED-EYE CONFIRMATION (CONTD.)
○ Red-eye confirmation is performed using a hue based
approach in HSI color space
○ In HSI space, hue value of 0° is standard red, but red-eye
pixels may show different color tones
○ For this reason, a pixel is considered as a red-eye pixel
with a hue value with ±5° deviation which is the
continuous arc from 5° to 355°
Fig. Range of Hue for Red-Eye Pixels
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19. RED-EYE CORRECTION
○ The research conducted by Patti et al. [5] shows that, during red-eye
effect only the red component of a pixel is damaged
○ We reconstructed the red component by assigning the average of
blue and green components
○ This gives the iris quite natural color (i.e. even preserves the glint in
the pupil)
Red-Eye
Correction
Fig. Result of Proposed Red-Eye Correction Method
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20. RESULTS & PERFORMANCE ANALYSIS
○ We have implemented our proposed method in the
following environment:
● Platform: Mac OS X, Linux (Ubuntu)
● Language: C++
● CPU: Intel Core i5 @ 2.5 GHz
● Memory: 4 GB
○ We tested the performance of our implemented system
on 50 images collected from various openly accessible
online image databases
○ Our system achieved overall 92% accuracy in red-eye
correction
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21. CONTRIBUTIONS
○ Our proposed system solves a classic problem in digital
photography using simple yet effective algorithms
○ It has achieved 92% red-eye correction accuracy while
being simple and fast
○ The system also implements a robust iris segmentation
method which can be used as a part of iris recognition
systems’ research
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22. FUTURE ENHANCEMENTS
○ Our system only works with human face but red-eye
problem can also be seen in animals. New research can
be done to adapt with this situation
○ The system performance degrades when the skin
segmentation cannot perform well due to any guarding
objects (e.g. spectacles or helmets). We will try to
improve our algorithm to adapt with this too
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23. REFERENCES
[1] H. Luo, J. Yen, D. Tretter, An Efficient Automatic Redeye Detection
and Correction Algorithm, IEEE International Conference on Pattern
Recognition (ICPR), Proceedings of the 17th International
Conference, Vol. 4, pp. 883-886, August 2004.
[2] J. Wan, X. Ren, G. Hu, Automatic Red-Eyes Detection Based on
AAM, Proc. IEEE Int Conf Systems, Man Cybernetics, pp. 6337-6341,
(2004)
[3] F. Volken, J. Terrier, P. Vandewalle, Automatic Red-Eye Removal
Based on Sclera and Skin Tone Detection, Proc. IS&T 3rd Eur. Conf.
Color Graphics, Imaging Vision (CGIV), pp. 359-364, (2006).
[4] L. Zhang, Y. Sun, M. Li, H. Zhang, Automated Red-Eye Detection
and Correction in Digital Photographs, Proc. IEEE Int. Conf. Image
Processing (ICIP), pp. 2363-2366, (2004).
[5] Patti, A., Kostantinides, K., Tretter, D., Lin, Q., Apparatus and A
Method for Reducing Red-Eye in A Digital Image, US6016354,
(2000).
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24. Thank You
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