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RED EYE DETECTIONAND CORRECTION

USING INPAINTING IN

DIGITAL

PHOTOGRAPHY .

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CONTENTS

Introduction Causes of red-eye effect

Factors affecting red-eye Red eye detection Analysis of the affected area

Red eye correction/removal Conclusion

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INTRODUCTION

When we take picture in a very low light weuse flash of the camera.After the snap the eyes present in the

pictures turns red (flashing red).The phenomenon is called red eye effect inphotography.

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E X AMPL ES OF R E D EYE E FF E CT INPHOTOGRAPH Y

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CAUSE OF RED EYE EFFECT:

Flash light passing through enlarged pupils isreflected on the blood vessels inside eyes, andarrives at a camera lens. This flash light makes

eyes in the photograph red.

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FACTOR S AFF E CTING R E D-EYE

The light reflected from the eye composesthe red-eye beacon. If a camera lens whichreceives the light is located inside the red-

eye beacon, pupils in the picture look red.In other words, red-eye effect occurs if :x

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RED-EYE DETECTION

The proposed red-eye detectionalgorithm is composed of threemodules:

Face detectionRed eye detection

Region-growing.

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F AC E DE T E CTION A face detection algorithm is used to limit thecandidate region for red-eye detection.

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R E D EYE DE T E CTION

1)Redness detection2 ) Skin colo r r emoval

3) Closing oper

ation4) Labeling 5) Shape filte r ing

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1) R EDNESS DETECTION

Redness is defined as:redness(x,y)=R^2(x,y)/G^2(x,y)+B^2(x,y)+K

where R, G, and B denote red, green, and bluecomponents,respectively, and k is a constant to avoid

zero denominator.The pixel at (x, y) is considered red if:

redness(x, y) >

where the value of redness threshold, is adjusted experimentally.

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2 ) S KIN COLOR REMOVAL

Since human skin often contains redcomponent much, redness is calculatedhigh in skin region. So there are many

false alarms in skin region. These falsealarms can be reduced by eliminatingskin color region.

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3) C LOSING OPERATION

Since previous two steps are pixelwise,result regions are scattered and difficult tohandle. Using closing morphologyoperation, neighboring regions are mergedand tiny holes in the regions are filled.

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4 ) L AB E LINGUsing labeling, we distinguish eachconnected component and take advantageof the information of size and shape of eachcomponent. Figure shows the result of thelabeling, in which each component regionhas different color.

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5 ) S HAP E FILT E RINGThe small red fragments fuses into largerunit.

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I NPAINTINGInpainting is a restoring process of damaged parts of an image.Here, red-eye regions are assumed to be a

damaged regions or missing regions.The regions lost their own color and texturein part by red-eye effect and expandedpupil in dark environment. By using aninpainting algorithm, these missing regionsshould be inpainted with iris texturenaturally.

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A FTER INPAINTING

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CONCLU S ION S

In this paper, it has been proposedthat an efficient red-eye removalalgorithm that uses an inpainting

method and biometric information.The distorted eye region is filled withnatural texture by an inpaintingmethod and biometric informationmakes it possible to find moreaccurate size of the original eyes.

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REFERENCESM . Gaubatz and R. Ulichney, A utomatic red-eyedetection and correction,H. Luo, J. Yen, and D. Tretter, A n efficientautomatic redeye detection and correctionalgorithm, in Proc. IEEE Int. Conf. PatternRecognition, vol. 2, pp. 883886, Cambridge, UK,

Aug. 2004.F. Volken, J. Terrier, and P. Vandewalle, A utomatic red-eye removal based on sclera and

skin tone detection, in Proc. European Conf.L. Zhang, Y. Sun, M . Li, and H. Zhang, A utomated red-eye detection and correction in

digital photographs