Iris Image Correction Method from Unconstrained Images

  • Eliana Frigerio
  • Marco Marcon
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 15)


The use of iris as biometric trait has emerged as one of the most preferred method because of its uniqueness, lifetime stability and regular shape. Moreover it shows public acceptance and new user-friendly capture devices are developed and used in a broadened range of applications. Currently, iris recognition systems work well with frontal iris images from cooperative users. Nonideal iris images are still a challenge for iris recognition and can significantly affect the accuracy of iris recognition systems. Moreover, accurate localization of different eye’s parts from videos or still images is a crucial step in many image processing applications that range from iris recognition in Biometrics to gaze estimation for Human Computer Interaction (HCI), impaired people aid or, even, marketing analysis for products attractiveness. Notwithstanding this, actually, most of available implementations for eye’s parts segmentation are quite invasive, imposing a set of constraints both on the environment and on the user itself limiting their applicability to high security Biometrics or to cumbersome interfaces. In the first part of this Chapter, we propose a novel approach to segment the sclera, the white part of the eye. We concentrate on this area since, thanks to the dissimilarity with other eye’s parts, its identification can be performed in a robust way against light variations, reflections and glasses lens flare. An accurate sclera segmentation is a fundamental step in iris and pupil localization, even in non-frontal noisy images. Furthermore its particular geometry can be fruitfully used for accurate eyeball rotation estimation. The proposed technique is based on a statistical approach (supported by some heuristic assumptions) to extract discriminating descriptors for sclera and non-sclera pixels. A Support Vector Machine (SVM) is then used as a final supervised classifier. Once the eyeball rotation angle respect to the camera optical axis is estimated and the limbus (the boundary between the iris and the sclera) is extracted, we propose a method to correct off-angle iris image. Taking into account the eye morphology and the reflectance properties of the external transparent layers, we can evaluate the distorting effects that are present in the acquired image. The correction algorithm proposed includes a first modeling phase of the human eye and a simulation phase where the acquisition geometry is reproduced and the distortions are evaluated. Finally we obtain an image which does not contain the distorting effects due to jumps in the refractive index. We show how this correction process reduces the intra-class variations for off-angle iris images.


Sclera segmentation Iris correction 3D eye model Gaze estimation Eikonal equation 


  1. 1.
    Benamou J (1995) Big ray tracing: Multivalued travel time field computation using viscosity solutions of the eikonal equation. J Comput Phys 128(2):463–474CrossRefMathSciNetGoogle Scholar
  2. 2.
    Colletto F, Marcon M, Sarti A, Tubaro S (2006) A robust method for the estimation of reliable wide baseline correspondences. Proc Int Conf Image Process 2006:1041–1044Google Scholar
  3. 3.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297MATHGoogle Scholar
  4. 4.
    Daugman J (2003) The importance of being random: statistical principles of iris recognition. Pattern Recogn 36(2):279–291CrossRefGoogle Scholar
  5. 5.
    Daugman J (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30CrossRefGoogle Scholar
  6. 6.
    Daugman J (2007) New methods in iris recognition. IEEE Trans Syst Man Cybern Part B Cybern 37(5):1167–1175CrossRefGoogle Scholar
  7. 7.
    Dobes M, Machala L (2004) Upol iris image database, link:
  8. 8.
    Dorairaj V, Schmid N, Fahmy G (2005) Performance evaluation of non-ideal iris based recognition system implementing global ica encoding. In: ICIP 2005. IEEE international conference on image processing. IEEE , 2005, vol 3, pp III 285Google Scholar
  9. 9.
    Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Wiley, New YorkGoogle Scholar
  10. 10.
    Gale A (1982) A note on the remote oculometer technique for recording eye movements. Vision Res 22(1):201–202Google Scholar
  11. 11.
    Gonzalez R, Woods R (1992) Digital image processing. Addison-Wesley Publishing Company, New YorkGoogle Scholar
  12. 12.
    Guestrin E, Eizenman M (2006) General theory of remote gaze estimation using the pupil center and corneal reflections. IEEE Trans Biomed Eng 53(6):1124–1133CrossRefGoogle Scholar
  13. 13.
    Hansen D, Ji Q (2010) In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans Pattern Anal Mach Intell 32(3):478–500CrossRefGoogle Scholar
  14. 14.
    Hiller F, Lieberman GJ (1995) Introduction to mathematical programming. McGraw-Hill, New YorkGoogle Scholar
  15. 15.
    Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8:179–187MATHGoogle Scholar
  16. 16.
    Krichen E, Mellakh M, Garcia-Salicetti S, Dorizzi B (2004) Iris identification using wavelet packets. In: ICPR 2004. Proceedings of the 17th international conference on pattern recognition, 2004. IEEE, vol 4, pp 335–338Google Scholar
  17. 17.
    Le Grand Y, Hunt R, Walsh J (1957) Light, colour and vision. Chapman & Hall, LondonGoogle Scholar
  18. 18.
    Li P, Ma H (2011) Iris recognition in non-ideal imaging conditions. Pattern Recogn Lett 33(8):1012–1018Google Scholar
  19. 19.
    Li X (2005) Modeling intra-class variation for nonideal iris recognition. Advances in biometrics. Springer, Berlin, pp 419–427Google Scholar
  20. 20.
    Liou H, Brennan N (1997) Anatomically accurate, finite model eye for optical modeling. JOSA A 14(8):1684–1695CrossRefGoogle Scholar
  21. 21.
    Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell 25(12):1519–1533CrossRefGoogle Scholar
  22. 22.
    Masek L et al (2003) Recognition of human iris patterns for biometric identification. Master’s thesis, The University of Western AustraliaGoogle Scholar
  23. 23.
    Mika S, Ratsch G, Weston J, Scholkopf B, Mullers K (1999) Fisher discriminant analysis with kernels. In: Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society, Workshop, pp 41–48Google Scholar
  24. 24.
    Morimoto C, Mimica M (2005) Eye gaze tracking techniques for interactive applications. Comput Vis Image Underst 98(1):4–24CrossRefGoogle Scholar
  25. 25.
    Park C, Lee J, Oh S, Song Y, Choi D, Park K (2003) Iris feature extraction and matching based on multiscale and directional image representation. In: Scale space methods in computer vision, Springer, pp 576–583.Google Scholar
  26. 26.
    Parker J, Duong A (2009) Gaze tracking: a sclera recognition approach. In: SMC 2009. IEEE international conference on systems, man and cybernetics, 2009. IEEE, pp. 3836–3841Google Scholar
  27. 27.
    Proença H, Alexandre LA (2004) Ubiris iris image database, link:
  28. 28.
    Roy K, Bhattacharya P (2009) Nonideal iris recognition using level set approach and coalitional game theory. In: Computer vision systems: 7th international conference on computer vision systems, ICVS 2009, Lecture Notes in Computer Science, vol 5815, pp 394–402.Google Scholar
  29. 29.
    Roy K, Bhattacharya P, Suen C (2011) Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical svms. Eng Appl Artif Intell 24(3):458–475CrossRefGoogle Scholar
  30. 30.
    Sanchez-Avila C, Sanchez-Reillo R (2005) Two different approaches for iris recognition using gabor filters and multiscale zero-crossing representation. Pattern Recogn 38(2):231–240CrossRefGoogle Scholar
  31. 31.
    Santos G, Hoyle E (2011) A fusion approach to unconstrained iris recognition. Pattern Recogn Lett 33(8):984–990Google Scholar
  32. 32.
    Schuckers S, Schmid N, Abhyankar A, Dorairaj V, Boyce C, Hornak L (2007) On techniques for angle compensation in nonideal iris recognition. IEEE Trans Syst Man Cybern Part B Cybern 37(5):1176–1190CrossRefGoogle Scholar
  33. 33.
    Shih S, Liu J (2004) A novel approach to 3-d gaze tracking using stereo cameras. IEEE Trans Syst Man Cybern Part B Cybern 34(1):234–245CrossRefGoogle Scholar
  34. 34.
    Shih S, Wu Y, Liu J (2000) A calibration-free gaze tracking technique. In: Proceedings of 15th international conference on pattern recognition, 2000. IEEE, vol 4, pp 201–204Google Scholar
  35. 35.
    Sung E, Chen X, Zhu J, Yang J (2002) Towards non-cooperative iris recognition systems. In: ICARCV 2002. 7th international conference on IEEE, vol 2, pp 990–995Google Scholar
  36. 36.
    Takano H, Kobayashi H, Nakamura K (2006) Iris recognition independent of rotation and ambient lighting variations. In: IJCNN’06. International joint conference on neural networks, 2006. IEEE, pp 4056–4062Google Scholar
  37. 37.
    Tan K, Kriegman D, Ahuja N (2002) Appearance-based eye gaze estimation. In: Proceedings of sixth IEEE workshop on applications of computer vision, 2002 (WACV 2002). IEEE, pp 191–195Google Scholar
  38. 38.
    Tan T (2010) Chinese accademy of science institute of automation casia iris database, link:
  39. 39.
    Vijaya Kumar B, Xie C, Thornton J (2003) Iris verification using correlation filters. In: Audio- and video-based biometric person authentication. Springer, pp 697–705Google Scholar
  40. 40.
    Villanueva A, Cabeza R (2008) A novel gaze estimation system with one calibration point. IEEE Trans Syst Man Cybern Part B Cybern 38(4):1123–1138CrossRefGoogle Scholar
  41. 41.
    Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–598CrossRefGoogle Scholar
  42. 42.
    Wang J, Sung E (2002) Study on eye gaze estimation. IEEE Trans Syst Man Cybern Part B Cybern 32(3):332–350CrossRefGoogle Scholar
  43. 43.
    Wang J, Sung E, Venkateswarlu R (2003) Eye gaze estimation from a single image of one eye. In: Proceedings of ninth IEEE international conference on computer vision, 2003. IEEE, pp 136–143Google Scholar
  44. 44.
    Wang J, Sung E, Venkateswarlu R (2005) Estimating the eye gaze from one eye. Comput Vis Image Underst 98(1):83–103CrossRefGoogle Scholar
  45. 45.
    Yu L, Zhang D, Wang K (2007) The relative distance of key point based iris recognition. Pattern Recogn 40(2):423–430CrossRefMATHGoogle Scholar
  46. 46.
    Zhao Y, Liu J, Li H, Li G (2008) Improved watershed algorithm for dowels image segmentation. In: WCICA 2008. 7th world congress on intelligent control and automation, 2008, p 7644Google Scholar
  47. 47.
    Zhu Z, Ji Q (2007) Novel eye gaze tracking techniques under natural head movement. IEEE Trans Biomed Eng 54(12):2246–2260CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.ISPG, DEIBPolitecnico di MilanoMilanoItaly

Personalised recommendations