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SIFT-based iris recognition revisited: prerequisites, advantages and improvements

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Abstract

Scale-invariant feature transform (SIFT), which represents a general purpose image descriptor, has been extensively used in the field of biometric recognition. Focusing on iris biometrics, numerous SIFT-based schemes have been presented in past years, offering an alternative approach to traditional iris recognition, which are designed to extract discriminative binary feature vectors based on an analysis of pre-processed iris textures. However, the majority of proposed SIFT-based systems fails to maintain the recognition accuracy provided by generic schemes. Moreover, traditional systems outperform SIFT-based approaches with respect to other key system factors, i.e. authentication speed and storage requirement. In this work, we propose a SIFT-based iris recognition system, which circumvents the drawbacks of previous proposals. Prerequisites, derived from an analysis of the nature of iris biometric data, are utilized to construct an improved SIFT-based baseline iris recognition scheme, which operates on normalized enhanced iris textures obtained from near-infrared iris images. Subsequently, different binarization techniques are introduced and combined to obtain binary SIFT-based feature vectors from detected keypoints and their descriptors. On the CASIAv1, CASIAv4-Interval and BioSecure iris database, the proposed scheme maintains the performance of different traditional systems in terms of recognition accuracy as well as authentication speed. In addition, we show that SIFT-based features complement those extracted by traditional schemes, such that a multi-algorithm fusion at score level yields a significant gain in recognition accuracy.

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References

  1. Alonso-Fernandez F, Tome-Gonzalez P, Ruiz-Albacete V, Ortega-Garcia J (2009) Iris recognition based on SIFT features. In: International conference on biometrics, identity and security (BIdS’09), pp 1–8

  2. Baber J, Dailey MN, Satoh S, Afzulpurkar N, Bakhtyar M (2014) BIG-OH: binarization of gradient orientation histograms. Image Vis Comput 32(11):940–953

    Article  Google Scholar 

  3. Baber J, Dailey MN, Satoh S, Afzulpurkar N, Bakhtyar M (2014) Big-oh: BInarization of gradient orientation histograms. Image Vis Comput 32(11):940–953

    Article  Google Scholar 

  4. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  5. Belcher C, Du Y (2009) Region-based SIFT approach to iris recognition. Opt Lasers Eng 47(1):139–147

    Article  Google Scholar 

  6. Bringer J, Despiegel V (2010) Binary feature vector fingerprint representation from minutiae vicinities. In: 4th IEEE International conference on biometrics: theory applications and systems (BTAS’10), pp 1–6

  7. Burge MJ, Bowyer K (2016) Handbook of iris recognition, 2nd edn. Springer, New York

    Google Scholar 

  8. Calonder M, Lepetit V, Strecha C, Fua P (2010) BRIEF: binary robust independent elementary features. In: Proceedings of the 11th European conference on computer vision (ECCV’10), pp 778–792

  9. Cappelli R, Ferrara M, Maltoni D (2010) Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans Pattern Anal Mach Intell 32(12):2128–2141

    Article  Google Scholar 

  10. Chen J, Shen F, Chen D, Flynn P (2016) Iris recognition based on human-interpretable features. IEEE Trans Inf Forensics Secur PP(99):1–10

    Google Scholar 

  11. Chen Y, Liu Y, Zhu X, He F, Wang H, Deng N (2014) Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion. Sci World J 2014:1–19

    Google Scholar 

  12. Chinese Academy of Sciences’ Institute of Automation (2002) CASIA Iris Image Database V1.0. http://biometrics.idealtest.org

  13. Chinese Academy of Sciences’ Institute of Automation (2002) CASIA Iris Image Database V3.0. http://biometrics.idealtest.org

  14. Chinese Academy of Sciences’ Institute of Automation (2012) CASIA Iris Image Database V4.0. http://biometrics.idealtest.org

  15. Daugman J (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161

    Article  Google Scholar 

  16. Daugman J (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30. https://doi.org/10.1109/ICIP.2002.1037952

    Article  Google Scholar 

  17. Dong W, Sun Z, Tan T (2011) Iris matching based on personalized weight map. IEEE Trans Pattern Anal Mach Intell 33(9):1744–1757

    Article  Google Scholar 

  18. Fierrez J, Ortega-Garcia J, Toledano DT, Gonzalez-Rodriguez J (2007) Biosec baseline corpus: a multimodal biometric database. Pattern Recognit 40(4):1389–1392

    Article  MATH  Google Scholar 

  19. Indian Institute of Technology Kanpur (2009) IITK Iris Image Database. http://www.cse.iitk.ac.in/users/biometrics/

  20. ISO/IEC JTC1 SC27 Security Techniques (2015) ISO/IEC 29794:2015. Information technology—biometric sample quality—part 6: iris image data. International Organization for Standardization

  21. ISO/IEC JTC1 SC27 Security Techniques (2016) ISO/IEC 30107-1:2016. Information technology—biometric presentation attack detection—part 1: framework. International Organization for Standardization

  22. ISO/IEC TC JTC1 SC37 Biometrics (2006) ISO/IEC 19795-1:2006. Information technology—biometric performance testing and reporting—part 1: principles and framework. International Organization for Standardization and International Electrotechnical Committee

  23. Jain AK, Flynn P, Ross AA (2008) Handbook of biometrics. Springer, New York

    Book  Google Scholar 

  24. Jain AK, Klare B, Ross AA (2015) Guidelines for best practices in biometrics research. In: Proceedings of the 8th international conference on biometrics 2015 (ICB’15), pp 1–5

  25. Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14:4–20. https://doi.org/10.1109/TCSVT.2003.818349

    Article  Google Scholar 

  26. Kong AWK, Zhang D, Kamel MS (2010) An analysis of iriscode. IEEE Trans Image Process 19(2):522–532

    Article  MathSciNet  Google Scholar 

  27. Krichen E, Mellakh MA, Garcia-Salicetti S, Dorizzi B (2004) Iris identification using wavelet packets. In: Proceedings of the 17th international conference on pattern recognition (ICPR’04), vol 4, pp 335–338

  28. Li J, Lu Z (2011) B-SIFT: a highly efficient binary SIFT descriptor for invariant feature correspondence. In: Workshop on intelligent science and intelligent data engineering, pp 426–433

  29. Li J, Lu Z (2012) Intelligent science and intelligent data engineering: second sino-foreign-interchange workshop, IScIDE 2011, chap. B-SIFT: a highly efficient binary SIFT descriptor for invariant feature correspondence, pp 426–433

  30. 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 An insight on eye biometrics

    Article  Google Scholar 

  31. Liu X, Li P (2012) An iris recognition approach with SIFT descriptors. In: 7th International conference on advanced intelligent computing theories and applications. With aspects of artificial intelligence, pp 427–434

  32. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  33. Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell 25(12):1519–1533

    Article  Google Scholar 

  34. Ma L, Tan T, Wang Y, Zhang D (2004) Efficient iris recognition by characterizing key local variations. IEEE Trans Image Process 13(6):739–750. https://doi.org/10.1109/TIP.2004.827237

    Article  Google Scholar 

  35. Mansfield A, Wayman J (2002) Best practices in testing and reporting performance of biometric devices. Tech. rep., U.K. Goverment Biometrics Working Group

  36. Masek L (2003) Recognition of human iris patterns for biometric identification. Master’s thesis, University of Western Australia

  37. Mehrotra H, Majhi B, Gupta P (2010) Robust iris indexing scheme using geometric hashing of SIFT keypoints. J Netw Comput Appl 33(3):300–313 Recent advances and future directions in biometrics personal identification

    Article  Google Scholar 

  38. Mehrotra H, Majhi B, Sa P (2011) Unconstrained iris recognition using F-SIFT. In: 8th International conference on information, communications and signal processing (ICICS’11), pp 1–5

  39. Monro DM, Rakshit S, Zhang D (2007) Dct-based iris recognition. IEEE Trans Pattern Anal Mach Intell 29(4):586–595

    Article  Google Scholar 

  40. Multimedia University (2004) MMU iris image database. http://pesona.mmu.edu.my/ccteo

  41. National Institute of Standards and Technology (NIST) (2005) Iris challenge evaluation (ICE). http://www.nist.gov/itl/iad/ig/ice.cfm

  42. Nguyen K, Fookes C, Ross A, Sridharan S (2017) Iris recognition with off-the-shelf cnn features: a deep learning perspective. IEEE Access PP(99):1–1

    Google Scholar 

  43. Nigam I, Vatsa M, Singh R (2015) Ocular biometrics: a survey of modalities and fusion approaches. Inf Fusion 26:1–35

    Article  Google Scholar 

  44. Ortega-Garcia J, Fierrez J, Alonso-Fernandez F, Galbally J, Freire MR, Gonzalez-Rodriguez J, Garcia-Mateo C, Alba-Castro JL, Gonzalez-Agulla E, Otero-Muras E, Garcia-Salicetti S, Allano L, Ly-Van B, Dorizzi B, Kittler J, Bourlai T, Poh N, Deravi F, Ng MW, Fairhurst M, Hennebert J, Humm A, Tistarelli M, Brodo L, Richiardi J, Drygajlo A, Ganster H, Sukno FM, Pavani SK, Frangi A, Akarun L, Savran A (2010) The multiscenario multienvironment biosecure multimodal database (BMDB). IEEE Trans Pattern Anal Mach Intell 32(6):1097–1111

    Article  Google Scholar 

  45. Park U, Pankanti S, Jain AK (2008) Fingerprint verification using sift features

  46. Proença H, Alexandre LA (2005) Ubiris: a noisy iris image database. In: Proceedings of the 13th international conference on image analysis and processing (ICIAP’05), pp 970–977

  47. Proença H, Filipe S, Santos R, Oliveira J, Alexandre L (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):1529–1535

    Article  Google Scholar 

  48. Raja KB, Raghavendra R, Stokkenes M, Busch C (2015) Multi-modal authentication system for smartphones using face, iris and periocular. In: Proceedings of the international conference on biometrics (ICB’15), pp 143–150

  49. Sun Z, Tan T (2009) Ordinal measures for iris recognition. IEEE Trans Pattern Anal Mach Intell 31(12):2211–2226

    Article  Google Scholar 

  50. Sunder MS, Ross A (2010) Iris image retrieval based on macro-features. In: Proceedings of the 20th international conference on pattern recognition (ICPR’10), pp 1318–1321

  51. Tomeo-Reyes I, Ross A, Clark A, Chandran V (2015) A biomechanical approach to iris normalization. In: International conference on biometrics (ICB’15), pp 9–16

  52. USIT (2016) University of Salzburg iris toolkit. http://www.wavelab.at/sources/Rathgeb16a. Version 2.0

  53. University of Bath (2004) University of Bath iris image database. http://www.bath.ac.uk/

  54. Vandewalle P, Kovacevic J, Vetterli M (2009) Reproducible research in signal processing—what, why, and how. IEEE Signal Process Mag 26(3):37–47

    Article  Google Scholar 

  55. West Virgina University (2005) WVU iris image database. http://www.csee.wvu.edu/

  56. Yang G, Pang S, Yin Y, Li Y, Li X (2012) SIFT based iris recognition with normalization and enhancement. Int J Mach Learn Cybern 4(4):401–407

    Article  Google Scholar 

  57. Yu L, Zhang D, Wang K (2007) The relative distance of key point based iris recognition. Elsevier Pattern Recognit 40(2):423–430

    Article  MATH  Google Scholar 

  58. Zhu R, Yang J, Wu R (2006) Iris recognition based on local feature point matching. In: International symposium on communications and information technologies (ISCIT’06), pp 451–454

  59. Zuiderveld K (1994) Graphics gems iv. chap. Contrast limited adaptive histogram equalization, pp 474–485

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Acknowledgements

Funding was partially provided by the German Federal Ministry of Education and Research (BMBF) as well as by the Hessen State Ministry for Higher Education, Research and the Arts (HMWK) within Center for Research in Security and Privacy (CRISP).

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  1. da/sec - Biometrics and Internet Security Research Group Hochschule Darmstadt, Darmstadt, Germany

    C. Rathgeb, J. Wagner & C. Busch

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  1. C. Rathgeb
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  2. J. Wagner
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  3. C. Busch
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Correspondence to C. Rathgeb.

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Rathgeb, C., Wagner, J. & Busch, C. SIFT-based iris recognition revisited: prerequisites, advantages and improvements. Pattern Anal Applic 22, 889–906 (2019). https://doi.org/10.1007/s10044-018-0719-y

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