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A Cluster-Based Boosting Strategy for Red Eye Removal

  • Sebastiano Battiato
  • Giovanni Maria Farinella
  • Daniele Ravì
  • Mirko Guarnera
  • Giuseppe Messina
Chapter

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.

Keywords

Color Space Face Detection Weak Classifier Gray Code Strong Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Mir, J. M.: Apparatus & method for minimizing red-eye in flash photography. U.S. Patent, no. US4285588 (1981)Google Scholar
  2. 2.
    Battiato, S., Bruna, A. R., Messina, G., Puglisi, G.: Image Processing for Embedded Devices. Bentham Science Publisher, Karachi (2010)Google Scholar
  3. 3.
  4. 4.
    Corel Paint Shop Pro, www.jasc.com
  5. 5.
  6. 6.
    Gasparini, F., Schettini, R.: Automatic red-eye removal for digital photography. In: Lukac, R. (ed.) Single-Sensor Imaging: Methods and Applications For Digital Cameras, pp. 429–457. CRC Press, Boston (2008)Google Scholar
  7. 7.
    Messina, G., Meccio, T.: Red eye removal. In: Battiato, S., Bruna, A.R., Messina, G., Puglisi, G. (eds.) Image Processing for Embedded Devices. Applied Digital Imaging Ebook Series. Bentham Science, Karachi (2010)Google Scholar
  8. 8.
    Gasparini, F., Schettini, R.: A review of redeye detection and removal in digital images through patents. Recent Pat. Electr. Eng. 2(1), 45–53 (2009)CrossRefGoogle Scholar
  9. 9.
    Battiato, S., Farinella, G.M., Guarnera, M., Messina, G., Ravì, D.: Red-eyes removal through cluster based linear discriminant analysis. In: International Conference on Image Processing (ICIP 2010), Hong Kong (2010)Google Scholar
  10. 10.
    Battiato, S., Farinella, G.M., Guarnera, M., Messina, G., Ravì, D.: Boosting gray codes for red eyes removal. In: International Conference on Pattern Recognition (ICPR 2010), Instanbul (TK) (2010)Google Scholar
  11. 11.
    Battiato, S., Farinella, G.M., Guarnera, M., Messina, G., Ravì, D.: Red-eyes removal through cluster based boosting on gray codes. EURASIP Journal on Image and Video Processing, Special Issue on Emerging Methods for Color Image and Video Quality Enhancement, 2010, pp. 1–11 (2010)Google Scholar
  12. 12.
    Zhang, L., Sun, Y., Li, M., Zhang, H.: Automated red-eye detection and correction in digital photographs. In: International Conference on Image Processing (2004)Google Scholar
  13. 13.
    Held, A.: Model-based correction of red-eye defects. In: IS&T Color Imaging Conference (CIC-02), pp. 223–228 (2002)Google Scholar
  14. 14.
    Gaubatz, M., Ulichney, R.: Automatic red-eye detection and correction. In: International Conference on Image Processing (2002)Google Scholar
  15. 15.
    Smolka, B., Czubin, K., Hardeberg, J.Y., Plataniotis, K.N., Szczepanski, M., Wojciechowski, K.W.: Towards automatic redeye effect removal. Pattern Recognit. Lett. 24(11), 1767–1785 (2003)CrossRefGoogle Scholar
  16. 16.
    Gasparini, F., Schettini, R.: Automatic redeye removal for smart enhancement of photos of unknown origin. In: Visual Information and Information Systems (VISUAL-2005). Lecture Notes in Computer Science, vol. 3736, pp. 226–233 (2005)Google Scholar
  17. 17.
    Willamowski, J., Csurka, G.: Probabilistic automatic red eye detection and correction. In: IEEE International Conference on Pattern Recognition (ICPR-06), pp. 762–765 (2006)Google Scholar
  18. 18.
    Patti, A. J., Konstantinides, K., Tretter, D., Lin, Q.: Automatic digital redeye reduction. In: International Conference on Image Processing, Chicago (1998)Google Scholar
  19. 19.
    Benati, P., Gray, R., Cosgrove, P.: Automated detection and correction of eye color defects due to flash illumination. U.S. Patent, no. US5748764 (1998)Google Scholar
  20. 20.
    Volken, F., Terrier, J., Vandewalle, P.: Automatic red-eye removal based on sclera and skin tone detection. In: European Conference on Color in Graphics, Imaging and Vision, pp. 359–364 (2006)Google Scholar
  21. 21.
    Ferman, A. M.: Automatic detection of red-eye artifacts in digital color photos. In: International Conference on Image Processing (2008)Google Scholar
  22. 22.
    Luo, H., Yen, J., Tretter, D.: An efficient automatic redeye detection and correction algorithm. In: International Conference on Pattern Recognition (2004)Google Scholar
  23. 23.
    Safonov, I.V.: Automatic red-eye detection. In: International conference on the Computer Graphics and Vision (2007)Google Scholar
  24. 24.
    Schildkraut, J. S., Gray, R. T.: A fully automatic redeye detection and correction algorithm. In: International Conference on Image Processing (2002)Google Scholar
  25. 25.
    Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)CrossRefGoogle Scholar
  26. 26.
    Hsu, R.L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)CrossRefGoogle Scholar
  27. 27.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  28. 28.
    Hongliang, L., Ngan, K.N., Qiang, L.: Faceseg: automatic face segmentation for real-time video. IEEE Trans. Multimed. 11(1), 77–88 (2009)CrossRefGoogle Scholar
  29. 29.
    Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)CrossRefGoogle Scholar
  30. 30.
    Corcoran, P., Bigioi, P., Steinberg, E., Pososin, A.: Automated in-camera detection of flash eye-defects. In: International Conference on Consumer Electronics (2005)Google Scholar
  31. 31.
    Safonov, I.V., Rychagova, M.N., Kang, K., Kim, S.H.: Automatic red eye correction and its quality metric. In: SPIE Electronic Imaging (2008)Google Scholar
  32. 32.
    Marchesotti, L., Bressan, M., Csurka, G.: Safe red-eye correction plug-in using adaptive methods. In: International Conference on Image Analysis and Processing—Workshops (ICIAPW-07), pp. 192–165 (2007)Google Scholar
  33. 33.
    Hardeberg, J.Y.: Red eye removal using digital color image processing. Image Processing, Image Quality, Image Capture, System Conference, Montreal, Canada, pp. 283–287 (2001)Google Scholar
  34. 34.
    Yoo, S., Park, R.-H.: Red-eye detection and correction using inpainting in digital photographs. IEEE Trans. Consum. Electr. 55(3), 1006–1014 (2009)CrossRefGoogle Scholar
  35. 35.
    Miao, X.-P., Sim, T.: Automatic red-eye detection and removal. In: International Conference on Multimedia and Expo (2004)Google Scholar
  36. 36.
    Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M. F., Hoppe, H., Toyama, K.: Digital photography with flash and no-flash image pairs. ACM Trans. Graph. 21(3), 673–678 (2004)Google Scholar
  37. 37.
    Saaty, T.L.: Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World, vol. 2. Analytic Hierarchy Process Series, New Edition (2001)Google Scholar
  38. 38.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)Google Scholar
  39. 39.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2008)Google Scholar
  40. 40.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 32, 102–107 (2000)Google Scholar
  41. 41.
    Schapire, R.E.: The boosting approach to machine learning: an overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2001)Google Scholar
  42. 42.
    Schapire, R.E.: The strength of weak learnability. In: Machine Learning, pp. 197–227 (1990)Google Scholar
  43. 43.
    Lienhart, R., Kuranov, E., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: DAGM 25th Pattern Recognition Symposium, pp. 297–304 (2003)Google Scholar
  44. 44.
    Torralba, A., Murphy, K.P.: Sharing visual features for multiclass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 854–869 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sebastiano Battiato
    • 1
  • Giovanni Maria Farinella
    • 1
  • Daniele Ravì
    • 1
  • Mirko Guarnera
    • 2
  • Giuseppe Messina
    • 2
  1. 1.Image Processing Laboratory (IPLAB) Dipartimento di Matematica e InformaticaUniversità di CataniaCataniaItaly
  2. 2.Advanced System Technology STMicroelectronicsCataniaItaly

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