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.
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Abdullah MAM, Dlay SS, Woo WL, Chambers JA (2016) A novel framework for cross-spectral iris matching. IPSJ Trans Comput Vis. Appl 8(1):9
Arivazhagan S, Ganesan L, Priyal SP (2006) Texture classification using Gabor wavelets based rotation invariant features. Pattern Recognit Lett 27(16):1976–1982
Bansal A, Agarwal R, Sharma RK (2016) Statistical feature extraction based iris recognition system. Sādhānā 41(5):507–518
Bansal M, Hanmandlu M, Kumar P (2016) IRIS based authentication using local principal independent components. Optik 127:4808–4814
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
Bowyer KW, Hollingsworth K, Flynn PJ (2008) Image understanding for iris biometrics: a survey. Comput Vis Image Underst 110(2):281–307
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 recognition
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–8
Daugman J (2003) The importance of being random: statistical principles of iris recognition. Pattern Recogn 36(2):279–291
Daugman J (2004) How iris recognition works. IEEE Trans Circ Syst Video Technol 14(1):21–30
Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161
Farouk RM (2011) Iris recognition based on elastic graph matching and Gabor wavelets. Comput Vis Image Underst 115(8):1239–1244
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 2301–2305
Huo G, Liu Y, Zhu X, Dong H (2015) Secondary iris recognition method based on local energy-orientation feature. J Electron Imaging 24(1):013,033–1–013,033–13
IITD iris database. http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm. Online; Accessed 20 July 2015
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):1–10
Karn P, He XH, Yang S, Wu XH (2014) Iris recognition based on robust principal component analysis. J Electron Imaging 23(6):063,002–1–063,002–8
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):111–128
Kulkarni SB, Kulkarni RB, Kulkarni UP, Hegadi RS (2014) GLCM-based multiclass iris recognition using FKNN and KNN. Int J Image Graph 14 (3):1450,010–1–1450,010–27
Liu N, Zhang M, Li H, Sun Z, Tan T (2016) DeepIris: learning pairwise filter bank for heterogeneous iris verification. Pattern Recognit Lett 82:154–161
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 101:44–51
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
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):1741–1756
Nalla PR, Kumar A (2017) Towards more accurate iris recognition using cross-spectral matching. IEEE Trans Image Process 26(1):208–221
Nguyen K, Fookes C, Jillela R, Sridharan S, Ross A (2017) Long range iris recognition: a survey. Pattern Recognit 72:123–143
Nigam A, Bendale A, Gupta P (2015) Efficient iris recognition system using relational measures. In: Computational forensics, pp 55–66
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 59:44–62
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
Rai H, Yadav A (2014) Iris recognition using combined support vector machine and Hamming distance approach. Expert Syst Appl 41(2):588–593
Shin KY, Kim YG, Park KR (2013) Enhanced iris recognition method based on multi-unit iris images. Opt Eng 52(4):047,201–1–047,201–11
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):3962–3974
The Hong Kong Polytechnic University Cross-Spectral Iris Images Database. http://www4.comp.polyu.edu.hk/~csajaykr/polyuiris.htm. Online; Accessed 7 Oct 2016
Umer S, Dhara BC, Chanda B (2015) Iris recognition using multiscale morphologic features. Pattern Recogn Lett 65:67–74
Umer S, Dhara BC, Chanda B (2016) Texture code matrix-based multi-instance iris recognition. Pattern Anal Appl 19(1):283–295
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)
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)
Wildes R (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85(9):1348–1363
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 5390–5399
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 3828–3836
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):1017–1030
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)
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.
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Vyas, R., Kanumuri, T. & Sheoran, G. Cross spectral iris recognition for surveillance based applications. Multimed Tools Appl 78, 5681–5699 (2019). https://doi.org/10.1007/s11042-018-5689-y
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DOI: https://doi.org/10.1007/s11042-018-5689-y