http://iet.metastore.ingenta.com
1887

Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset

Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The historical NEXUS iris kiosks log dataset collected by the Canada Border Services Agency from 2003 to 2014 has become the focus of scientific attention due to its involvement in the iris ageing debate between the National Institute of Standard and Technology and the University of Notre Dame researchers. To facilitate this debate, this study provides additional details on how this dataset was collected, its various properties and irregularities, and presents new results related to the effect of ageing, age, and other factors on the system performance obtained using the portions of the dataset that have not been previously analysed. In doing that, the importance of conducting subject-based performance analysis, as opposed to the traditionally done transaction-based analysis, is emphasised. The significance of factor effects is examined. Recommendations on further improvement of the technology are made.

References

    1. 1)
      • 1. Canada Border Services Agency’. NEXUS Air Available at www.cbsa-asfc.gc.ca/prog/nexus/air-aerien-eng.html, Accessed: 6 November 2017.
    2. 2)
      • 2. Grother, P., Matey, J.R., Tabassi, E., et al: ‘IREX VI. Temporal stability of iris recognition accuracy’, NIST Interagency Report 7948, 2013.
    3. 3)
      • 3. IET Biometrics Journal’, November 2017, Iris Ageing Debate in IET Biometrics Available at http://www.theiet.org/resources/irisageing.cfm, accessed September 2015.
    4. 4)
      • 4. Grother, P., Matey, J.R., Quinn, G.W.: ‘IREX VI: mixed-effects longitudinal models for iris ageing: response to Bowyer and Ortiz’, IET Biometrics, 2015, 4, (4), pp. 200205.
    5. 5)
      • 5. Bowyer, K., Ortis, E.: ‘Critical examination of the IREX VI results’, IET Biometrics, 2015, 4, (4), pp. 192199.
    6. 6)
      • 6. Ortis, E., Bowyer, K.: ‘Exploratory analysis of an operational iris recognition dataset from a CBSA border-crossing application’. IEEE Computer Society Biometrics Workshop, Boston, June 2015.
    7. 7)
      • 7. Czajka, A., Bowyer, K.: ‘Statistical evaluation of up-to-three-attempt iris recognition’. IEEE Int. Conf. Biometrics Theory, Applications and Systems BTAS, Washington DC, 2015.
    8. 8)
      • 8. Kuehlkamp, A., Bowyer, K.: ‘An analysis of 1-to-first matching in iris recognition’. IEEE Workshop on Applications of Computer Vision, March 2016.
    9. 9)
      • 9. Ortiz, E., Bowyer, K.: ‘Pitfalls in studying big data from operational scenarios’. IEEE Int. Conf. Biometrics Theory, Applications and Systems BTAS, Washington DC, 2016.
    10. 10)
      • 10. Canada Border Services Agency’. CANPASS Air Available at www.cbsa-asfc.gc.ca/prog/canpass/canpassair-eng.html, Accessed: 6 November 2017.
    11. 11)
      • 11. Rathgeb, C.: ‘A biometric for life potential for a lifetime breeder document’. Int. Biometric Performance Testing Conf. (IBPC), Gaithersburg, 2014.
    12. 12)
      • 12. International Joint Conference on Biometrics (IJCB) 2014 Keynote speaker presentations. Available at http://www.ijcb2014.org/Keynote_Speakers.html (S. Lenharo ‘Brazilian National Biometric Selection: New and Legacy Challenges’, V.S. Madan ‘Digital ID for Benefit and Service Delivery to Billion Plus People’, S. Braiki ‘The UAE Population Register and ID Card Program: Achievements and the Challenges’, W.G. McKinsey (‘The Challenges of NGI’), Accessed: 6 November 2017.
    13. 13)
      • 13. Wild, P., Ferryman, J., Uhl, A.: ‘Impact of (segmentation) quality on long vs. shorttime span assessments in iris recognition performance’, IET Biometrics, 2015, 4, (4), pp. 227235.
    14. 14)
      • 14. Baker, S., Bowyer, K., Flynn, P.: ‘Empirical evidence for correct iris match score degradation with increased time-lapse between gallery and probe matches’. Proc. Int. Conf. Biometrics (ICB), Alghero, Italy, 2009, pp. 11701179.
    15. 15)
      • 15. Baker, S., Bowyer, K., Flynn, P., et al: ‘Empirical evidence for increased false reject rate with time lapse in ICE 2006’, NIST Interagency Report 7752, 2011.
    16. 16)
      • 16. Fenker, S., Ortis, E., Bowyer, K.: ‘Template aging phenomenon in iris recognition’, IEEE Access, 2013, 1, pp. 266274.
    17. 17)
      • 17. Researchers reawaken iris-ageing debate’. Available at http://www.planetbiometrics.com/article-details/i/3439/desc/researchers-reawaken-iris-ageing-debate, accessed 30 November 2015.
    18. 18)
      • 18. Aged eyes prevent iris recognition. Healthy seniors’. Available at http://www.healthyolderpersons.org/news/aged-eyes-reventiris-rec, accessed 7 March 2012.
    19. 19)
      • 19. Aging process confounds iris recognition biometrics’, Homeland security newswire. Available at http://www.homelandsecuritynewswire.com/dr20120531-aging-process-confounds-iris-recognition-biometrics, accessed 31 May 2012.
    20. 20)
      • 20. Researchers question long-term reliability of iris recognition’, Third factor. Available at https://www.secureidnews.com/news-item/researchers-question-long-term-reliability-of-iris-recognition/, accessed 17 July 2012.
    21. 21)
      • 21. Browning, K., Orlans, N.: ‘Biometric aging effects of aging on iris recognition’. Case Number 13–3472, 2014. The MITRE Corporation. Available at https://www.mitre.org/sites/default/files/publications/13-3472-biometric-aging-iris-recognition.pdf, Accessed: 6 November 2017.
    22. 22)
      • 22. Daugman, J.: ‘How iris recognition works’, IEEE Trans. Circuits Syst. Video Technol., 2002, 14, pp. 2130.
    23. 23)
      • 23. Daugman, J.: ‘Probing the uniqueness and randomness of IrisCodes: results from 200 billion iris pair comparisons’, Proc. IEEE, 2006, 94, (11), pp. 19271935.
    24. 24)
      • 24. Daugman, J.: ‘New methods in iris recognition’, IEEE Trans. Syst. Man Cybern. B, 2007, 37, (5), pp. 11671175.
    25. 25)
      • 25. Daugman, J.: ‘Information theory and the IrisCode’, IEEE Trans. Inf. Forensics Sec., 2015, pp. 400409.
    26. 26)
      • 26. Gorodnichy, D., Hoshino, R.: ‘Score calibration for optimal biometric identification’. Proc. Canadian Conf. Artificial Intelligence (AI 2010), Ottawa, 2010, pp. 357361.
    27. 27)
      • 27. Gorodnichy, D.: ‘ART in ABC: analysis of risks and trends in automated border control’. Technical Report DRDC-RDDC-2016-C324 (Full report), 2016. Available at http://cradpdf.drd-rddc.gc.ca/PDFS/unc256/p804885_A1b.pdf. Technical Report DRDC-RDDC-2016-C143D (Executive Summary): http://cradpdf.drdc-rddc.gc.ca/PDFS/unc229/p803869_A1b.pdf, Accessed: 6 November 2017.
    28. 28)
      • 28. ISO/IEC 19795-5, Information Technology – ‘Biometric Performance Testing and Reporting Part-5: Grading scheme for Access Control Scenario Evaluation’.
    29. 29)
      • 29. Doddington, G., Liggett, W., Martin, A., et al: ‘Sheep, goats, lambs and wolves: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation’. Proc. Fifth Int. Conf. Spoken Language Processing, ICSLP 98, Sydney, Australia, 1998.
    30. 30)
      • 30. Poh, N.: ‘IEEE IJCB tutorial system design and performance assessment: a biometric menagerie perspective’. IJCB 2014 Conf., Clearwater, Florida. Available at http://ijcb2014.org/Tutorials.html, Accessed: 6 November 2017.
    31. 31)
      • 31. Gorodnichy, D.: ‘Multi-order biometric score analysis framework and its application to designing and evaluating biometric systems for access and border control’. Proc. IEEE SSCI Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM), Orlando, April 2011.
    32. 32)
      • 32. Gorodnichy, D., Bissessar, D., Granger, E., et al: ‘Recognizing people and their activities in surveillance video: technology state of readiness and roadmap’. Proc. 13th Conf. Computer and Robot Vision (CRV), Victoria, Canada, 2016. Available at http://www.videorecognition.com/doc, Accessed: 6 November 2017.
    33. 33)
      • 33. Grolemund, G., Wickham, H.: ‘R for data science’ (O'Reilly, 2017, 1st edn.).
    34. 34)
      • 34. ISO/IEC TR 22116, information technology – ‘Identifying and mitigating the differential impact of demographic factors in biometric systems’. Available at https://www.iso.org/standard/72604.html.
    35. 35)
      • 35. Wood, S.N.: ‘Generalized additive models: an introduction with R’ (Chapman and Hall/CRC, Boca Raton, Florida, 2006).
    36. 36)
      • 36. Treasury Board Secretariat of Canada, Gender-Based Analysis Plus. Available at https://www.tbs-sct.gc.ca/hgw-cgf/oversight-surveillance/tbs-pct/gba-oacs-eng.asp, Accessed: 6 November 2017.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5105
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5105
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address