Multimedia Tools and Applications

, Volume 78, Issue 6, pp 6655–6677 | Cite as

An adaptive localization of pupil degraded by eyelash occlusion and poor contrast

  • Gunjan GautamEmail author
  • Susanta Mukhopadhyay


The inner boundary of iris represents the pupil’s edge. Hence, to work an Iris Recognition System (IRS) and the gaze tracking system expeditiously it is important to locate it as precisely as possible in a significant amout of time. In the presence of non-ideal constraints e.g. non-uniform illumination, poor contrast, eyelashes, hairs, glasses, off-angle orientation, these systems may not work well. In this paper we present an adaptive pupil localization method based on the roundness criteria. First, it applies a gray level inversion to suppress the reflections, then it performs Gray level co-occurrence matrix (GLCM) based contrast estimation. If this estimated contrast is lower than a certain threshold, the input image is made to undergo gamma correction to adjust the contrast. Subsequently, anisotropic diffusion filtering followed by log transformation is applied, which suppresses the effect of eyelash occlusion, limits the creation of small regions and highlight the dark pixels. Afterwards, a clean binary image with few regions is acquired using adaptive thresholding and some morphological operations. Finally, the roundness metric is computed for each of these regions and the region with largest roundness metric, also being greater than a prescribed threshold, declared as pupil. Experiments were carried out on few well known databases, NICE1, CASIA V3 lamp, MMU, WVU and IITD. The results are grounded upon subjective and objective evaluation; which in turn, indicate that our method outperforms a state-of-the-art approach and a deep learning approach in terms of localization capability in some unconstrained scenarios and shorter processing time. After assessing the performance of the proposed algorithm, it is manifested that it ensures a fast and robust localization of pupil in the presence of corneal reflection, poor contrast, glasses and eyelash occlusion.


Iris biometric Pupil localization Gray-level co-occurrence matrix Contrast estimation Morphological reconstruction 



  1. 1.
    Bowyer KW, Burge MJ (2016) Handbook of iris recognition. Springer, BerlinCrossRefGoogle Scholar
  2. 2.
    Bowyer KW, Hollingsworth K, Flynn PJ (2008) Image understanding for iris biometrics: A survey. Comput Vis Image Underst 110(2):281–307CrossRefGoogle Scholar
  3. 3.
    Bowyer KW, Hollingsworth KP, Flynn PJ (2016) A survey of iris biometrics research: 2008–2010. In: Handbook of iris recognition, Springer, pp 23–61Google Scholar
  4. 4.
    CASIA (Webpage) Iris image database.
  5. 5.
    Chen B, Yang Z, Huang S, Du X, Cui Z, Bhimani J, Xie X, Mi N (2017) Cyber-physical system enabled nearby traffic flow modelling for autonomous vehicles. In: 2017 IEEE 36th international performance computing and communications conference (IPCCC), IEEE, pp 1–6Google Scholar
  6. 6.
    Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst Hum 43(4):996–1002CrossRefGoogle Scholar
  7. 7.
    Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161CrossRefGoogle Scholar
  8. 8.
    Daugman J (2003) The importance of being random: statistical principles of iris recognition. Pattern Recogn 36(2):279–291CrossRefGoogle Scholar
  9. 9.
    Daugman J (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30CrossRefGoogle Scholar
  10. 10.
    Ding M, Fan G (2015) Multilayer joint gait-pose manifolds for human gait motion modeling. IEEE Trans Cybern 45(11):2413–2424CrossRefGoogle Scholar
  11. 11.
    Ding M, Fan G (2016) Articulated and generalized Gaussian kernel correlation for human pose estimation. IEEE Trans Image Process 25(2):776–789MathSciNetCrossRefGoogle Scholar
  12. 12.
    Frucci M, Nappi M, Riccio D, di Baja GS (2016) Wire: watershed based iris recognition. Pattern Recogn 52:148–159CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Haralick RM, Shanmugam K, et al. (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621CrossRefGoogle Scholar
  15. 15.
    Jan F, Usman I, Agha S (2013) Reliable iris localization using hough transform, histogram-bisection, and eccentricity. Signal Process 93(1):230–241CrossRefGoogle Scholar
  16. 16.
    Jeong M, Nam JY, Ko BC (2017) Eye pupil detection system using an ensemble of regression forest and fast radial symmetry transform with a near infrared camera. Infrared Physics & TechnologyGoogle Scholar
  17. 17.
    Kacete A, Royan J, Seguier R, Collobert M, Soladie C (2016) Real-time eye pupil localization using hough regression forest. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1–8Google Scholar
  18. 18.
    Khan TM, Khan MA, Malik SA, Khan SA, Bashir T, Dar AH (2011) Automatic localization of pupil using eccentricity and iris using gradient based method. Opt Lasers Eng 49(2):177–187CrossRefGoogle Scholar
  19. 19.
    Kooshkestani S, Pooyan M, Sadjedi H (2008) A new method for iris recognition systems based on fast pupil localization. Computational Science and Its Applications–ICCSA 2008:555–564Google Scholar
  20. 20.
    Kovesi P (2004) Matlab functions for computer vision and image analysisGoogle Scholar
  21. 21.
    Laboratory BR (Webpage) Iit delhi iris database.
  22. 22.
    Lifshitz LM, Pizer SM (1988) A multiresolution hierarchical approach to image segmentation based on intensity extrema. In: Information processing in medical imaging, Springer, pp 107–130Google Scholar
  23. 23.
    Lin Z, Yu H (2011) The pupil location based on the otsu method and hough transform. Prog Environ Sci 8:352–356CrossRefGoogle Scholar
  24. 24.
    Lindeberg T (1994) Scale-space theory: a basic tool for analyzing structures at different scales. J Appl Stat 21(1-2):225–270CrossRefGoogle Scholar
  25. 25.
    Liu X, Bowyer KW, Flynn PJ (2005) Experiments with an improved iris segmentation algorithm. In: 2005 4th IEEE workshop on automatic identification advanced technologies. IEEE, pp 118-123Google Scholar
  26. 26.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: recognizing complex activities from sensor data. In: IJCAI, vol 2015, pp 1617–1623Google Scholar
  27. 27.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  28. 28.
    Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. In: AAAI, vol 2016, pp 201–207Google Scholar
  29. 29.
    Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learningGoogle Scholar
  30. 30.
    Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440Google Scholar
  31. 31.
    Markuš N, Frljak M, Pandžić IS, Ahlberg J, Forchheimer R (2014) Eye pupil localization with an ensemble of randomized trees. Pattern Recog 47(2):578–587CrossRefGoogle Scholar
  32. 32.
    Martinez F, Carbone A, Pissaloux E (2012) Gaze estimation using local features and non-linear regression. In: 2012 19th IEEE international conference on image processing (ICIP). IEEE, pp 1961–1964Google Scholar
  33. 33.
    Masek L, Kovesi P (2003) Matlab source code for a biometric identification system based on iris patterns. The School of Computer Science and Software Engineering, The University of Western Australia 2(4)Google Scholar
  34. 34.
    Masters BR, Gonzalez RC, Woods R (2009) Digital image processing. J Biomed Opt 14(2):029, 901CrossRefGoogle Scholar
  35. 35.
    MMU (Webpage) Multimedia-university.
  36. 36.
    Moos S, Marcolin F, Tornincasa S, Vezzetti E, Violante MG, Fracastoro G, Speranza D, Padula F (2017) Cleft lip pathology diagnosis and foetal landmark extraction via 3d geometrical analysis. Int J Interact Des Manuf 11(1):1–18CrossRefGoogle Scholar
  37. 37.
    Olsen OF (1997) Multi-scale watershed segmentation. In: Gaussian scale-space theory, Springer, pp 191–200Google Scholar
  38. 38.
    Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  39. 39.
    Proenca H, Alexandre LA (2012) Toward covert iris biometric recognition: experimental results from the nice contests. IEEE Trans Inf Forensics Secur 7(2):798–808CrossRefGoogle Scholar
  40. 40.
    Proenca H, Filipe S, Santos R, Oliveira J, Alexandre LA (2010) The ubiris. v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans Pattern Anal Mach Intell 32(8):1529CrossRefGoogle Scholar
  41. 41.
    Raj M, Semwal VB, Nandi G (2016) Bidirectional association of joint angle trajectories for humanoid locomotion: the restricted boltzmann machine approach. Neural Computing and Applications pp 1–9Google Scholar
  42. 42.
    Semwal VB, Raj M, Nandi GC (2015) Biometric gait identification based on a multilayer perceptron. Robot Auton Syst 65:65–75CrossRefGoogle Scholar
  43. 43.
    Semwal VB, Kumar C, Mishra PK, Nandi GC (2016) Design of vector field for different subphases of gait and regeneration of gait pattern. IEEE Transactions on Automation Science and EngineeringGoogle Scholar
  44. 44.
    Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput and Appl 28(3):565–574CrossRefGoogle Scholar
  45. 45.
    Semwal VB, Singha J, Sharma PK, Chauhan A, Behera B (2017) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl 76(22):24,457–24,475CrossRefGoogle Scholar
  46. 46.
    Soille P (2013) Morphological image analysis: principles and applications. Springer Science & Business Media, BerlinzbMATHGoogle Scholar
  47. 47.
    Song F, Tan X, Chen S, Zhou ZH (2013) A literature survey on robust and efficient eye localization in real-life scenarios. Pattern Recogn 46(12):3157–3173CrossRefGoogle Scholar
  48. 48.
    Tian D, He G, Wu J, Chen H, Jiang Y (2016) An accurate eye pupil localization approach based on adaptive gradient boosting decision tree. In: Visual communications and image processing (VCIP), 2016, IEEE, pp 1–4Google Scholar
  49. 49.
    Tsiotsios C, Petrou M (2013) On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recog 46(5):1369–1381CrossRefGoogle Scholar
  50. 50.
    University WV (Webpage) Iris image database.
  51. 51.
    Valenti R, Sebe N, Gevers T (2012) Combining head pose and eye location information for gaze estimation. IEEE Trans Image Process 21(2):802–815MathSciNetCrossRefGoogle Scholar
  52. 52.
    Vater S, León FP (2016) Combining isophote and cascade classifier information for precise pupil localization. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 589–593Google Scholar
  53. 53.
    Vezzetti E, Marcolin F, Tornincasa S, Ulrich L, Dagnes N (2017) 3d geometry-based automatic landmark localization in presence of facial occlusions. Multimedia Tools and Applications pp 1–29Google Scholar
  54. 54.
    Weickert J (1998) Anisotropic diffusion in image processing, vol 1. Teubner StuttgartGoogle Scholar
  55. 55.
    Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85(9):1348–1363CrossRefGoogle Scholar
  56. 56.
    Xie X, Liu S, Yang C, Yang Z, Xu J, Zhai X (2017) The application of smart materials in tactile actuators for tactile information delivery. arXiv:170807077
  57. 57.
    Zhang C, Sun X, Hu J, Deng W (2014) Precise eye localization by fast local linear svm. In: 2014 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1-6Google Scholar
  58. 58.
    Zhang W, Chen H, Yao P, Li B, Zhuang Z (2006) Precise eye localization with adaboost and fast radial symmetry. In: International conference on computational and information science, Springer, pp 1068–1077CrossRefGoogle Scholar
  59. 59.
    Zhao Y, Qu Z, Han H, Yuan L (2016) An effective and rapid localization algorithm of pupil center based on starburst model. In: 2016 IEEE advanced information management, communicates, electronic and automation control conference (IMCEC). IEEE, pp 988–991Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of TechnologyDhanbadIndia

Personalised recommendations