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Abstract

This paper offers a brief audit of biometric technology in the post-pandemic realm. To accomplish the audit objectives, we map a general biometric-enabled system concept onto the Emergency Management Cycle (EMC), a core doctrine to address disasters. This mapping helps identify the technology-societal gaps unveiled during the most recent pandemic. We focus on auditing the biometric-enabled watchlist for e-borders and e-health systems. In the biometric-enabled systems, fairness becomes of critical importance, while the related concept of Equity, Diversity, and Inclusion (EDI) is well suited for the generalization of fairness in biometrics. We also emphasize the need to update the biometric courses for training future technology developers.

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References

  1. Abid, N., Shmerko, V., Yanushkevich, S.: Audit of computational intelligence techniques for EDI-aware systems. In: Proceedings of IEEE International Joint Conference on Neural Networks, Padua, Italy, pp. 1–8 (2022)

    Google Scholar 

  2. Alonso-Fernandez, F., Raja, K.B., Raghavendra, R., Busch, C., et al.: Cross-Sensor Periocular Biometrics for Partial Face Recognition in a Global Pandemic: Comparative Benchmark and Novel Multialgorithmic Approach, arXiv arXiv:1902.08123 (2020)

  3. Andersson, R.: Europe’s failed ‘fight’ against irregular migration: ethnographic notes on a counterproductive industry. J. Ethn. Migr. Stud. 42(7), 1055–1075 (2016)

    Article  Google Scholar 

  4. Baral, P.: Health systems and services during COVID-19: lessons and evidence from previous crises: a rapid scoping review to inform the united nations research roadmap for the COVID-19 recovery. Int. J. Health Serv. 51(4), 474–493 (2021)

    Article  Google Scholar 

  5. Bernstein, R.S., Bulger, M., Salipante, P., Weisinger, J.Y.: From diversity to inclusion to equity: a theory of generative interactions. J. Bus. Ethics 167, 395–410 (2020)

    Article  Google Scholar 

  6. Beun, R.J., et al.: Improving adherence in automated e-coaching. In: Meschtscherjakov, A., De Ruyter, B., Fuchsberger, V., Murer, M., Tscheligi, M. (eds.) PERSUASIVE 2016. LNCS, vol. 9638, pp. 276–287. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31510-2_24

    Chapter  Google Scholar 

  7. Butt, M., et al.: Towards e-passport duplicate enrollment check in the European Union. In: Proceedings of European Intelligence and Security Informatics Conference, pp. 247–251 (2013)

    Google Scholar 

  8. Carpenter-Song, E., et al.: Individualized intervention to support mental health recovery through implementation of digital tools into clinical care: feasibility study. Community Ment. Health J. 58(1), 99–110 (2022)

    Article  Google Scholar 

  9. Char, D.S., Shah, N.H., Magnus, D.: Implementing machine learning in health care - addressing ethical challenges. N. Engl. J. Med. 378, 981–983 (2018)

    Article  Google Scholar 

  10. Cho, J.-H., Chan, K., Adali, S.: A survey on trust modeling. ACM Comput. Surv. 48(2), 1–40 (2015)

    Article  Google Scholar 

  11. Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)

    Article  Google Scholar 

  12. Clavell, G.G.: Protect rights at automated borders. Nature 543(7643), 34–36 (2017)

    Article  Google Scholar 

  13. Committee of the Whole: Binder for the Minister of Public Safety and Emergency Preparedness - July 8, 2020: Facial Recognition, Public Safety Canada (2020)

    Google Scholar 

  14. Correll, J., Park, B., Judd, C.M., et al.: Across the thin blue line: police officers and racial bias in the decision to shoot. J. Pers. Soc. Psychol. 92, 1006–1023 (2007)

    Article  Google Scholar 

  15. Das, A., Dantcheva, A., Bremond, F.: Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach. In: European Conference on Computer Vision, pp. 1–13 (2018)

    Google Scholar 

  16. Drozdowski, P., Rathgeb, C., Busch, C.: Demographic Fairness in Face Identification: The Watchlist Imbalance Effect, arXiv, arXiv:2106.08049 (2021)

  17. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: ITCS 2012: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214–226 (2012)

    Google Scholar 

  18. European Union: Technical Study on Smart Borders. European Commission, Directorate General for Home Affairs and PwC (2014)

    Google Scholar 

  19. Estevens, J.: Migration crisis in the EU: developing a framework for analysis of national security and defence strategies. Comp. Migr. Stud. 6, 28 (2018)

    Article  Google Scholar 

  20. Fagel, M.J.: Principles of Emergency Management and Emergency Operations Centers (EOC). CRC Press, Boca Raton (2010)

    Book  Google Scholar 

  21. Faurholt-Jepsen, M., et al.: The effect of smartphone-based monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trial. Psychol. Med. 50, 838–848 (2020)

    Article  Google Scholar 

  22. Frontex, Operational and technical security of electronic passports. Frontex Research and Development Unit, Warsaw (2011)

    Google Scholar 

  23. Fu, Y., Guo, G., Huang, T.S.: Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955–1976 (2010)

    Article  Google Scholar 

  24. Fu, S., He, H., Hou, Z.-G.: Learning race from face: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2483–2509 (2014)

    Article  Google Scholar 

  25. Grother, P., Ngan, M., Hanaoka, K.: Face recognition vendor test (FRVT) part 3: demographic effects. National Institute of Standards and Technology Interagency or Internal Report 8280 (2019)

    Google Scholar 

  26. Halberstadt, G., Cooke, A.N., Garner, P.W., et al.: Racialized emotion recognition accuracy and anger bias of children’s faces. Emot. Am. Psychol. Assoc. 22(3), 403–41 (2022)

    Google Scholar 

  27. Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Adv. Neural. Inf. Process. Syst. 29, 3315–3323 (2016)

    Google Scholar 

  28. Howard, J.J., Rabbitt, L.R., Sirotin, Y.B.: Human-algorithm teaming in face recognition: how algorithm outcomes cognitively bias human decision-making. PLoS ONE 15(8), e0237855 (2020)

    Article  Google Scholar 

  29. Hugenberg, K., Wilson, J.P., See, P.E., Young, S.G.: Towards a synthetic model of own group biases in face memory. Vis. Cogn. 21(9–10), 1392–1417 (2013)

    Article  Google Scholar 

  30. IATA (International Air Transport Association), Travel-Pass Initiative (2021)

    Google Scholar 

  31. ICAO (International Civil Aviation Organization), Machine Readable Travel Documents, Seventh Edition, Document 9303 (2015)

    Google Scholar 

  32. International Organization for Standardization ISO 19011:2018, Guidelines for auditing management systems (2018)

    Google Scholar 

  33. Ivanov, D.: Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains. Ann. Oper. Res. 1–18 (2021)

    Google Scholar 

  34. Jain, A.K., Nandakumar, K., Ross, A.: 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn. Lett. 79, 80–105 (2016)

    Article  Google Scholar 

  35. Jillela, R., Ross, A.: Mitigating effects of plastic surgery: fusing face and ocular biometrics. In: Proceedings of IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, pp. 402–411 (2012)

    Google Scholar 

  36. Kamphorst, B.A.: Autonomy-Respectful E-Coaching Systems: Fending Off Complacency. Ph.D. thesis, Utrecht university (2020)

    Google Scholar 

  37. Kamphorst, B.: E-coaching systems: what they are, and what they aren’t. Pers. Ubiquit. Comput. 21(1), 625–632 (2017)

    Article  Google Scholar 

  38. Krishnan, A., Almadan, A., Rattani, A.: Probing fairness of mobile ocular biometrics methods across gender on VISOB 2.0 dataset. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12668, pp. 229–243. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68793-9_16

    Chapter  Google Scholar 

  39. Labati, R.D., Genovese, A., Muniz, E., Piuri, V., Scotti, F., Sforza, G.: Biometric recognition in automated border control: a survey. ACM Comput. Surv. 49(2), 1–39 (2016)

    Article  Google Scholar 

  40. Lai, K., Oliveira, H.C., Hou, M., Yanushkevich, S.N., Shmerko, V.: Assessing risks of biases in cognitive decision support systems. In: European Signal Processing Conference, pp. 840–844 (2021)

    Google Scholar 

  41. Lai, K., Yanushkevich, S.N., Shmerko, V.P.: Epidemic attack on the aircraft carrier theodore roosevelt: bridging the gaps in emergency management. J. Def. Model. Simul. (2021)

    Google Scholar 

  42. Lai, K., Yanushkevich, S., Shmerko, V., Eastwood, S.: Bridging the gap between forensics and biometric-enabled watchlists for e-borders. IEEE Comput. Intell. Mag. 12(1), 17–28 (2017)

    Article  Google Scholar 

  43. Lai, K., Kanich, O., Dvorak, M., Drahansky, M., et al.: Biometric-enabled watchlists technology. IET Biometrics 6(6), 1–10 (2017)

    Google Scholar 

  44. Liebana-Cabanillas, F., Munoz-Leiva, F., Molinillo, S., Higueras-Castillo, E.: Do biometric payment systems work during the COVID-19 pandemic? Insights from the Spanish users’ viewpoint. Financ. Innov. 8(22), 1–25 (2022)

    Google Scholar 

  45. Martinez, B., Valstar, M., Jiang, B., Pantic, M.: Automatic analysis of facial actions: a survey. IEEE Trans. Affect. Comput. 10(3), 325–347 (2019)

    Article  Google Scholar 

  46. McLeman, R.: International migration and climate adaptation in an era of hardening borders. Nat. Clim. Chang. 9, 911–918 (2019)

    Article  Google Scholar 

  47. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54, 1–35 (2019)

    Article  Google Scholar 

  48. Merler, M., Ratha, N., Feris, R.S., Smith, J.R.: Diversity in faces, arXiv arXiv:1901.10436 (2019)

  49. Meyer, A., Wisniewski, H., Torous, J.: Coaching to support mental health apps: exploratory narrative review. JMIR Hum. Factors 9(1), e28301 (2022)

    Article  Google Scholar 

  50. Moon, H.G., Lho, H.L., Han, H.: Selfcheck-in kiosk quality and airline non-contact service maximization: how to win air traveler satisfaction and loyalty in the post-pandemic world? J. Travel Tour. Mark. 38(4), 383–398 (2021)

    Article  Google Scholar 

  51. National Institute of Standards (NIST), Security and Privacy Controls for Information Systems and Organizations. NIST Special Publication 800-53, Rev. 5 (2017)

    Google Scholar 

  52. Oberschmidt, K., Grünloh, C., Nijboer, F., van Velsen, L.: Best practices and lessons learned for action research in ehealth design and implementation: literature review. J. Med. Internet Res. 24(1), 31795 (2022)

    Article  Google Scholar 

  53. Osoba, O.A., Welser, W.: An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence. RAND Corporation (2017)

    Google Scholar 

  54. Pereira, T.F., Marcel, S.: Fairness in biometrics: a figure of merit to assess biometric verification systems. IEEE Trans. Biom. Behav. Identity Sci. 4(1), 19–29 (2022)

    Article  Google Scholar 

  55. Queiroz, L., Lai, K., Yanushkevich, S., Shmerko, V.: Biometrics in the Time of Pandemic: 40% Masked Face Recognition Degradation can be Reduced to 2%, arXiv, arXiv:2201.00461 (2022)

  56. Rathgeb, C., Drozdowski, P., Frings, D.C., Damer, N., Busch, C.: Demographic Fairness in Biometric Systems: What do the Experts say? arXiv, arXiv:2105.14844 (2021)

  57. Ross, A., Rattani, A., Tistarelli, M.: Exploiting the Doddington zoo effect in biometric fusion. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, pp. 1–7 (2009)

    Google Scholar 

  58. Sa, C., Cowley, S., Martinez, M., Kachynska, N., Sabzalieva, E.: Gender gaps in research productivity and recognition among elite scientists in the US, Canada, and South Africa. PLoS One 15(10), 2020 (2020)

    Article  Google Scholar 

  59. Salari, N., et al.: Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. J. Glob. Health 16(57) (2020)

    Google Scholar 

  60. Scheirer, W.: A pandemic of bad science. Bull. Atomic Sci. 76(4), 175–184 (2020)

    Article  Google Scholar 

  61. Tanase, F.-D., Demyen, S., Manciu, V.-C., Tanase, A.-C.: Online education in the COVID-19 pandemic-premise for economic competitiveness growth? Sustainability 14(6), 3503 (2022)

    Article  Google Scholar 

  62. Terhorst, P., Kolf, J.N., Damer, N., Kirchbuchner, F., Kuijper, A.: Post-comparison mitigation of demographic bias in face recognition using fair score normalization. Pattern Recogn. Lett. 140, 332–338 (2020)

    Article  Google Scholar 

  63. Thevenot, J., Lopez, M.B., Hadid, A.: A survey on computer vision for assistive medical diagnosis from faces. IEEE J. Biomed. Health Inform. 22(5), 1497–1511 (2018)

    Article  Google Scholar 

  64. US Department of Homeland Security, National Response Framework (2022). media-library/assets/documents/117791

    Google Scholar 

  65. Valdivia, A., Corbera-Serrajordia, J., Swianiewicz, A.: There is an elephant in the room: towards a critique on the use of fairness in biometrics, arXiv, arXiv:2112.11193 (2021)

  66. VanVactor, J.D.: Strategic healthcare logistics planning in emergency management. Disaster Prev. Manag. 21(3), 299–309 (2012)

    Article  Google Scholar 

  67. Varma, P., Junge, M., Meaklim, H., Jackson, M.L.: Younger people are more vulnerable to stress, anxiety and depression during COVID-19 pandemic: a global cross-sectional survey. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 109, 110236 (2021)

    Article  Google Scholar 

  68. Verma, S., Rubin, J.: Fairness definitions explained. In: IEEE/ACM International Workshop on Software Fairness, pp. 1–7 (2018)

    Google Scholar 

  69. Yager, N., Dunstone, T.: The biometric menagerie member. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 220–230 (2010)

    Article  Google Scholar 

  70. Yanushkevich, S.N., Eastwood, S.C., Manderson, T.L., et al.: Taxonomy and modeling of impersonation in e-border authentication. In: Sixth International Conference on Emerging Security Technologies, pp. 38–43 (2015)

    Google Scholar 

  71. Yanushkevich, S., Reitinger, N., Stoica, A., et al.: Inverse biometrics: privacy, risks, and trust. In: Encyclopedia of Cryptography, Security and Privacy, pp. 1–4 (2020)

    Google Scholar 

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Acknowledgments

This project was partially supported by the Natural Sciences and Engineering Research Council of Canada through the grant “Biometric intelligent interfaces”, and the Social Sciences and Humanities Research Council of Canada via NFRF project “Emergency Management Cycle-Centric R&D: From National Prototyping to Global Implementation”.

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Correspondence to Svetlana Yanushkevich .

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Gnanasekar, S.T., Shaposhnyk, O., Yankovyi, I., Yanushkevich, S. (2023). Brief Audit of Post-pandemic Biometrics. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_44

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