Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5681–5699 | Cite as

Cross spectral iris recognition for surveillance based applications

  • Ritesh Vyas
  • Tirupathiraju KanumuriEmail author
  • Gyanendra Sheoran
Article
  • 253 Downloads

Abstract

Human iris has been explored as one of the most promising biometric traits since last many years. This paper presents a new ingenious feature extraction approach which is based on the texture variations of the iris template. A 2D Gabor filter bank is first employed to reveal the iris texture at different scales and orientations. Each filtered iris template is then partitioned into smaller sub-blocks. Contemplating the iris texture variations at micro-levels, two-level template partitioning is employed here. Difference of Variance (DoV) of corresponding first and second level sub-blocks, from each filtered image, then forms the feature set of the iris. Performance of the proposed iris recognition scheme is first tested with the benchmark IITD iris database to find the optimal window size of the filter bank. Thereafter, to prove the efficacy of the proposed approach in surveillance based applications, cross-spectral iris matching experiments (i.e. visible wavelength (VW) to near infrared (NIR) matching) are performed using PolyU cross-spectral database. Experiments show that the proposed approach achieves outperforming results for both IITD and PolyU databases.

Keywords

Iris recognition Difference of Variance (DoV) Template partitioning Surveillance Cross-spectral matching 

Notes

Acknowledgements

The authors would like to thank Indian Institute of Technology Delhi (IITD) and Hong-Kong Polytechnic University (PolyU) for providing access to their iris databases.

References

  1. 1.
    Abdullah MAM, Dlay SS, Woo WL, Chambers JA (2016) A novel framework for cross-spectral iris matching. IPSJ Trans Comput Vis. Appl 8(1):9CrossRefGoogle Scholar
  2. 2.
    Arivazhagan S, Ganesan L, Priyal SP (2006) Texture classification using Gabor wavelets based rotation invariant features. Pattern Recognit Lett 27(16):1976–1982CrossRefGoogle Scholar
  3. 3.
    Bansal A, Agarwal R, Sharma RK (2016) Statistical feature extraction based iris recognition system. Sādhānā 41(5):507–518MathSciNetzbMATHGoogle Scholar
  4. 4.
    Bansal M, Hanmandlu M, Kumar P (2016) IRIS based authentication using local principal independent components. Optik 127:4808–4814CrossRefGoogle Scholar
  5. 5.
    Barpanda SS, Sa PK, Marques O, Majhi B, Bakshi S (2017) Iris recognition with tunable filter bank based feature. Multimed Tools Appl 1–38.  https://doi.org/10.1007/s11042-017-4668-z CrossRefGoogle Scholar
  6. 6.
    Bowyer KW, Hollingsworth K, Flynn PJ (2008) Image understanding for iris biometrics: a survey. Comput Vis Image Underst 110(2):281–307CrossRefGoogle Scholar
  7. 7.
    Boyce C, Ross A, Monaco M, Hornak L, Xin L (2006) Multispectral iris analysis: a preliminary study. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognitionGoogle Scholar
  8. 8.
    Burge MJ, Monaco MK (2009) Multispectral iris fusion for enhancement, interoperability, and cross wavelength matching. In: Algorithms and technologies for multispectral, hyperspectral and ultraspectral imagery, vol 7334, pp 73,341D–1–73,341D–8Google Scholar
  9. 9.
    Daugman J (2003) The importance of being random: statistical principles of iris recognition. Pattern Recogn 36(2):279–291CrossRefGoogle Scholar
  10. 10.
    Daugman J (2004) How iris recognition works. IEEE Trans Circ Syst Video Technol 14(1):21–30CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Farouk RM (2011) Iris recognition based on elastic graph matching and Gabor wavelets. Comput Vis Image Underst 115(8):1239–1244CrossRefGoogle Scholar
  13. 13.
    Gangwar A, Joshi A (2016) DeepIrisNet: deep iris representation with applications in iris recognition and cross-sensor iris recognition. In: 2016 IEEE International conference image processing, pp 2301–2305Google Scholar
  14. 14.
    Huo G, Liu Y, Zhu X, Dong H (2015) Secondary iris recognition method based on local energy-orientation feature. J Electron Imaging 24(1):013,033–1–013,033–13CrossRefGoogle Scholar
  15. 15.
    IITD iris database. http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm. Online; Accessed 20 July 2015
  16. 16.
    Kang BJ, Park KR, Yoo JH, Moon K (2010) Fuzzy difference-of-Gaussian-based iris recognition method for noisy iris images. Opt Eng 49 (6):1–10Google Scholar
  17. 17.
    Karn P, He XH, Yang S, Wu XH (2014) Iris recognition based on robust principal component analysis. J Electron Imaging 23(6):063,002–1–063,002–8CrossRefGoogle Scholar
  18. 18.
    Khalighi S, Pak F, Tirdad P, Nunes U (2014) Iris recognition using robust localization and nonsubsampled contourlet based features. J Signal Process Syst 81 (1):111–128CrossRefGoogle Scholar
  19. 19.
    Kulkarni SB, Kulkarni RB, Kulkarni UP, Hegadi RS (2014) GLCM-based multiclass iris recognition using FKNN and KNN. Int J Image Graph 14 (3):1450,010–1–1450,010–27CrossRefGoogle Scholar
  20. 20.
    Liu N, Zhang M, Li H, Sun Z, Tan T (2016) DeepIris: learning pairwise filter bank for heterogeneous iris verification. Pattern Recognit Lett 82:154–161CrossRefGoogle Scholar
  21. 21.
    Llano EG, Vázquez MSG, Vargas JMC, Fuentes LMZ, Acosta AAR (2018) Optimized robust multi-sensor scheme for simultaneous video and image iris recognition. Pattern Recognit Lett 101:44–51CrossRefGoogle Scholar
  22. 22.
    Luz E, Moreira G, Antonio L, Junior Z, Menotti D (2017) Deep periocular representation aiming video surveillance. Pattern Recognit Lett 1–11.  https://doi.org/10.1016/j.patrec.2017.12.009 CrossRefGoogle Scholar
  23. 23.
    Miyazawa K, Ito K, Aoki T, Kobayashi K, Nakajima H (2008) An effective approach for Iris recognition using phase-based image matching. IEEE Trans Pattern Anal Mach Intell 30(10):1741–1756CrossRefGoogle Scholar
  24. 24.
    Nalla PR, Kumar A (2017) Towards more accurate iris recognition using cross-spectral matching. IEEE Trans Image Process 26(1):208–221MathSciNetCrossRefGoogle Scholar
  25. 25.
    Nguyen K, Fookes C, Jillela R, Sridharan S, Ross A (2017) Long range iris recognition: a survey. Pattern Recognit 72:123–143CrossRefGoogle Scholar
  26. 26.
    Nigam A, Bendale A, Gupta P (2015) Efficient iris recognition system using relational measures. In: Computational forensics, pp 55–66CrossRefGoogle Scholar
  27. 27.
    Pham TTT, Le TL, Vu H, Dao TK, Nguyen VT (2017) Fully-automated person re-identification in multi-camera surveillance system with a robust kernel descriptor and effective shadow removal method. Image Vis Comput 59:44–62CrossRefGoogle Scholar
  28. 28.
    Qi M, Han J, Jiang J, Liu H (2017) Deep feature representation and multiple metric ensembles for person re-identification in security surveillance system. Multimed Tools Appl 1–15.  https://doi.org/10.1007/s11042-017-4649-2
  29. 29.
    Rai H, Yadav A (2014) Iris recognition using combined support vector machine and Hamming distance approach. Expert Syst Appl 41(2):588–593CrossRefGoogle Scholar
  30. 30.
    Shin KY, Kim YG, Park KR (2013) Enhanced iris recognition method based on multi-unit iris images. Opt Eng 52(4):047,201–1–047,201–11CrossRefGoogle Scholar
  31. 31.
    Tan CW, Kumar A (2014) Accurate iris recognition at a distance using stabilized iris encoding and Zernike moments phase features. Image Process IEEE Trans 23 (9):3962–3974MathSciNetCrossRefGoogle Scholar
  32. 32.
    The Hong Kong Polytechnic University Cross-Spectral Iris Images Database. http://www4.comp.polyu.edu.hk/~csajaykr/polyuiris.htm. Online; Accessed 7 Oct 2016
  33. 33.
    Umer S, Dhara BC, Chanda B (2015) Iris recognition using multiscale morphologic features. Pattern Recogn Lett 65:67–74CrossRefGoogle Scholar
  34. 34.
    Umer S, Dhara BC, Chanda B (2016) Texture code matrix-based multi-instance iris recognition. Pattern Anal Appl 19(1):283–295MathSciNetCrossRefGoogle Scholar
  35. 35.
    Vyas R, Kanumuri T, Sheoran G (2016) Iris recognition using 2-D Gabor filter and XOR-SUM code. In: IEEE India international conference on information processing (IICIP)Google Scholar
  36. 36.
    Vyas R, Kanumuri T, Sheoran G, Dubey P (in press) Iris recognition through score-level fusion. In: IAPR Second International conference on computer vision, graphics and image processing (CVIP-2017)Google Scholar
  37. 37.
    Wildes R (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85(9):1348–1363CrossRefGoogle Scholar
  38. 38.
    Wu A, Zheng WS, Yu HX, Gong S, Lai J (2017) RGB-infrared cross-modality person re-identification. In: 2017 IEEE International conference on computer vision, pp 5390–5399Google Scholar
  39. 39.
    Zhao Z, Kumar A (2015) An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In: IEEE international conference on computer vision, pp 3828–3836Google Scholar
  40. 40.
    Zhao Z, Kumar A (2017) Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network. IEEE Trans Inf Forensics Secur 12(5):1017–1030CrossRefGoogle Scholar
  41. 41.
    Zuo J, Nicolo F, Schmid NA (2010) Cross spectral iris matching based on predictive image mapping. In: IEEE 4th international conference on biometrics: theory, applications and systems (BTAS)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.National Institute of Technology DelhiNarelaIndia

Personalised recommendations