Pervasive and useR fOcused biomeTrics bordEr projeCT (PROTECT) is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this study illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores.

References

1. 1)
  - 23. Debiasi, L., Kauba, C., Prommegger, B., et al: ‘Near-infrared illumination add-on for mobile hand-vein acquisition’. 2018 IEEE 9th Int. Conf. on Biometrics Theory, Applications and Systems (BTAS), Los Angeles, California, USA, 2018, pp. 1–9.
2. 2)
  - 11. Xiong, X., Torre, F.D.L.: ‘Supervised descent method and its applications to face alignment’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Portland, OR, USA., 2013, pp. 532–539.
3. 3)
  - 22. Kauba, C., Uhl, A.: ‘Shedding light on the veins – reflected light or transillumination in hand-vein recognition’. Proc. 11th IAPR/IEEE Int. Conf. on Biometrics (ICB'18), Gold Coast, Queensland, Australia, 2018, pp. 1–8.
4. 4)
  - 14. Park, U., Ross, A., Jain, A. K.: ‘Periocular biometrics in the visible spectrum: a feasibility study’. 2009 IEEE 3rd Int. Conf. on Biometrics: Theory, Applications and Systems, Washington, DC, USA., 2009, pp. 1–6.
5. 5)
  - 7. Amos, B., Ludwiczuk, B., Satyanarayanan, M.: ‘Openface: A general-purpose face recognition library with mobile applications’, 2016.
6. 6)
  - 16. Raghavendra, R., Busch, C.: ‘Robust 2d/3d face mask presentation attack detection scheme by exploring multiple features and comparison score level fusion’. 17th Int. Conf. on Information Fusion (FUSION), Salamanca, Spain, 2014, pp. 1–7.
7. 7)
  - 8. Chiesa, V., Dugelay, J.: ‘On multi-view face recognition using Lytro images’. 2018 26th European Signal Processing Conf. (EUSIPCO), Rome, Italy, September 2018, pp. 2250–2254.
8. 8)
  - 13. Zhang, L., Chu, R., Xiang, S., et al: ‘Face detection based on multi-block LBP representation’. Int. Conf. on Biometrics, Seoul, Republic of Korea, 2007, pp. 11–18.
9. 9)
  - 10. Ren, S., He, K., Girshick, R.B., et al: ‘Faster R-CNN: towards real-time object detection with region proposal networks’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 39, (6), pp. 1137–1149.
10. 10)
  - 27. Zuiderveld, K.: ‘Contrast limited adaptive histogram equalization’, in Heckbert, P.S. (Ed.): ‘Graphics gems IV’ (Morgan Kaufmann, USA., 1994), pp. 474–485.
11. 11)
  - 26. Zhang, J., Yang, J.: ‘Finger-vein image enhancement based on combination of gray-level grouping and circular Gabor filter’. Int. Conf. on Information Engineering and Computer Science, 2009 ICIECS, Wuhan, People's Republic of China, 2009, pp. 1–4.
12. 12)
  - 9. Galdi, C., Chiesa, V., Busch, C., et al: ‘Light fields for face analysis’, Sensors, 2019, 19, (12), Article ID: 2687.
13. 13)
  - 29. Kauba, C., Reissig, J., Uhl, A.: ‘Pre-processing cascades and fusion in finger vein recognition’. Proc. Int. Conf. of the Biometrics Special Interest Group (BIOSIG'14), Darmstadt, Germany, September 2014.
14. 14)
  - 3. Jain, A.K., Ross, A.: ‘Multibiometric systems’, Commun. ACM, 2004, 47, (1), pp. 34–40.
15. 15)
  - 19. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A comparative study of texture measures with classification based on featured distributions’, Pattern Recognit., 1996, 29, (1), pp. 51–59.
16. 16)
  - 21. Kauba, C., Prommegger, B., Uhl, A.: ‘Openvein - an open-source modular multipurpose finger vein scanner design’, In Uhl, A., Busch, C., Marcel, S., et al (Eds.): ‘Handbook of vascular biometrics’ (Springer Nature Switzerland AG, Cham, Switzerland, 2019), pp. 77–111, chapter 3.
17. 17)
  - 2. International Civil Aviation Organization (ICAO).: ‘Machine readable travel documents’, in ‘Doc 9303’ (International Civil Aviation Organization, Canada, 2015, 7th Edn.), pp. 8–16.
18. 18)
  - 28. Miura, N., Nagasaka, A., Miyatake, T.: ‘Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification’, Mach. Vis. Appl., 2004, 15, (4), pp. 194–203.
19. 19)
  - 20. Kumar, A., Zhou, Y.: ‘Human identification using finger images’, IEEE Trans. Image Process., 2012, 21, (4), pp. 2228–2244.
20. 20)
  - 18. Raja, K.B., Raghavendra, R., Busch, C.: ‘Binarized statistical features for improved iris and periocular recognition in visible spectrum’. 2nd Int. Workshop on Biometrics and Forensics, Valletta, Malta, 2014, pp. 1–6.
21. 21)
  - 5. Sequeira, A.F., Chen, L., Ferryman, J., et al: ‘Protect multimodal db: fusion evaluation on a novel multimodal biometrics dataset envisaging border control’. 2018 Int. Conf. of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, September 2018, pp. 1–5.
22. 22)
  - 17. Tan, C.-W., Kumar, A.: ‘Towards online iris and periocular recognition under relaxed imaging constraints’, IEEE Trans. Image Process., 2013, 22, (10), pp. 3751–3765.
23. 23)
  - 25. Zhao, J., Tian, H., Xu, W., et al: ‘A new approach to hand vein image enhancement’. In Second Int. Conf. on Intelligent Computation Technology and Automation, 2009 ICICTA'09, Changsha, Hunan, People's Republic of China, 2009, vol. 1, pp. 499–501.
24. 24)
  - 15. Popplewell, K., Alford, A., Dozier, G.V., et al: ‘A comparison of genetic feature selection and weighting techniques for multibiometric recognition’. ACM Southeast Regional Conf., Kennesaw, GA, USA., 2011, pp. 205–208.
25. 25)
  - 30. Ding, Y., Ross, A.: ‘A comparison of imputation methods for handling missing scores in biometric fusion’, Pattern Recognit., 2012, 45, (3), pp. 919–933.
26. 26)
  - 4. International Electrotechnical Commission (IEC) International Organization for Standardization (ISO). Iso/iec 19795 information technology—biometric performance testing and reporting.
27. 27)
  - 6. Kauba, C., Uhl, A.: ‘An available open-source vein recognition framework’ (Springer International Publishing, Cham, 2020), pp. 113–142.
28. 28)
  - 1. Jain, A.K., Ross, A., Prabhakar, S.: ‘An introduction to biometric recognition’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 4–20.
29. 29)
  - 24. Lu, Y., Xie, S. J., Yoon, S., et al: ‘Robust finger vein roi localization based on flexible segmentation’, Sensors, 2013, 13, (11), pp. 14339–14366.
30. 30)
  - 12. Assirati, L., da Silva, N.R., Berton, L., et al: ‘Performing edge detection by difference of gaussians using q-Gaussian kernels’. Journal of Physics: Conf. Series, 2014, Vol. 490, p. 012020, IOP Publishing, 2014.

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT – a case study

References

Related content