Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset
- Author(s): Dmitry O. Gorodnichy 1 and Michael P. Chumakov 2
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View affiliations
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Affiliations:
1:
Science and Engineering Directorate , Canada Border Services Agency , Ottawa , Canada ;
2: Business Application Services Directorate , Canada Border Services Agency , Ottawa , Canada
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Affiliations:
1:
Science and Engineering Directorate , Canada Border Services Agency , Ottawa , Canada ;
- Source:
Volume 8, Issue 1,
January
2019,
p.
29 – 39
DOI: 10.1049/iet-bmt.2018.5105 , Print ISSN 2047-4938, Online ISSN 2047-4946
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.
Inspec keywords: iris recognition; human factors
Other keywords: factor effects; NEXUS scores dataset; iris recognition performance; iris ageing; historical NEXUS iris kiosks; Canada Border Services Agency; dataset collection; system performance; subject-based performance analysis; transaction-based analysis
Subjects: Computer vision and image processing techniques; Image recognition
References
-
-
1)
-
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.
-
-
2)
-
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.
-
-
3)
-
35. Wood, S.N.: ‘Generalized additive models: an introduction with R’ (Chapman and Hall/CRC, Boca Raton, Florida, 2006).
-
-
4)
-
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.
-
-
5)
-
33. Grolemund, G., Wickham, H.: ‘R for data science’ (O'Reilly, 2017, 1st edn.).
-
-
6)
-
1. ‘Canada Border Services Agency’. NEXUS Air Available at www.cbsa-asfc.gc.ca/prog/nexus/air-aerien-eng.html, Accessed: 6 November 2017.
-
-
7)
-
8. Kuehlkamp, A., Bowyer, K.: ‘An analysis of 1-to-first matching in iris recognition’. IEEE Workshop on Applications of Computer Vision, March 2016.
-
-
8)
-
10. ‘Canada Border Services Agency’. CANPASS Air Available at www.cbsa-asfc.gc.ca/prog/canpass/canpassair-eng.html, Accessed: 6 November 2017.
-
-
9)
-
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.
-
-
10)
-
18. ‘Aged eyes prevent iris recognition. Healthy seniors’. Available at http://www.healthyolderpersons.org/news/aged-eyes-reventiris-rec, accessed 7 March 2012.
-
-
11)
-
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. 227–235.
-
-
12)
-
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.
-
-
13)
-
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.
-
-
14)
-
11. Rathgeb, C.: ‘A biometric for life potential for a lifetime breeder document’. Int. Biometric Performance Testing Conf. (IBPC), Gaithersburg, 2014.
-
-
15)
-
23. Daugman, J.: ‘Probing the uniqueness and randomness of IrisCodes: results from 200 billion iris pair comparisons’, Proc. IEEE, 2006, 94, (11), pp. 1927–1935.
-
-
16)
-
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.
-
-
17)
-
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.
-
-
18)
-
2. Grother, P., Matey, J.R., Tabassi, E., et al: ‘IREX VI. Temporal stability of iris recognition accuracy’, NIST Interagency Report 7948, 2013.
-
-
19)
-
25. Daugman, J.: ‘Information theory and the IrisCode’, IEEE Trans. Inf. Forensics Sec., 2015, pp. 400–409.
-
-
20)
-
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. 1170–1179.
-
-
21)
-
16. Fenker, S., Ortis, E., Bowyer, K.: ‘Template aging phenomenon in iris recognition’, IEEE Access, 2013, 1, pp. 266–274.
-
-
22)
-
26. Gorodnichy, D., Hoshino, R.: ‘Score calibration for optimal biometric identification’. Proc. Canadian Conf. Artificial Intelligence (AI 2010), Ottawa, 2010, pp. 357–361.
-
-
23)
-
3. ‘IET Biometrics Journal’, November 2017, Iris Ageing Debate in IET Biometrics Available at http://www.theiet.org/resources/irisageing.cfm, accessed September 2015.
-
-
24)
-
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.
-
-
25)
-
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.
-
-
26)
-
24. Daugman, J.: ‘New methods in iris recognition’, IEEE Trans. Syst. Man Cybern. B, 2007, 37, (5), pp. 1167–1175.
-
-
27)
-
5. Bowyer, K., Ortis, E.: ‘Critical examination of the IREX VI results’, IET Biometrics, 2015, 4, (4), pp. 192–199.
-
-
28)
-
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.
-
-
29)
-
28. ISO/IEC 19795-5, Information Technology – ‘Biometric Performance Testing and Reporting Part-5: Grading scheme for Access Control Scenario Evaluation’.
-
-
30)
-
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.
-
-
31)
-
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.
-
-
32)
-
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. 200–205.
-
-
33)
-
22. Daugman, J.: ‘How iris recognition works’, IEEE Trans. Circuits Syst. Video Technol., 2002, 14, pp. 21–30.
-
-
34)
-
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.
-
-
35)
-
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.
-
-
36)
-
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.
-
-
1)