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Document scanners for minutiae-based palmprint recognition: a feasibility study

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Abstract

Highly expensive capturing devices and barely existent high-resolution palmprint datasets have slowed the development of forensic palmprint biometric systems in comparison with civilian systems. These issues are addressed in this work. The feasibility of using document scanners as a cheaper option to acquire palmprints for minutiae-based matching systems is explored. A new high-resolution palmprint dataset was established using an industry-standard Green Bit MC517 scanner and an HP Scanjet G4010 document scanner. Furthermore, a new enhancement algorithm to attenuate the negative effect of creases in the process of minutiae extraction is proposed. Experimental results highlight the potentialities of document scanners for forensic applications. Advantages and disadvantages of both technologies are discussed in this context as well.

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  1. http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm.

References

  1. Aguado-Martínez M, Hernández-Palancar J, Castillo-Rosado K, Kauba C, Kirchgasser S, Uhl A (2019) On using document scanners for minutiae-based palmprint recognition. In: Progress in pattern recognition, image analysis, computer vision, and applications: 24th Iberoamerican congress, CIARP 2019, Havana, Cuba, October 28–31, 2019, Proceedings, vol 11896, p 219. Springer Nature

  2. Cappelli R, Ferrara M, Maio D (2012) A fast and accurate palmprint recognition system based on minutiae. IEEE Trans Syst Man Cybern B 42(3):956–962

    Article  Google Scholar 

  3. Choraś M, Kozik R (2012) Contactless palmprint and knuckle biometrics for mobile devices. Pattern Anal Appl 15(1):73–85

    Article  MathSciNet  Google Scholar 

  4. Dai J, Zhou J (2011) Multifeature-based high-resolution palmprint recognition. IEEE Trans Pattern Anal Mach Intell 33(5):945–957

    Article  Google Scholar 

  5. Derawi MO, Yang B, Busch C (2011) Fingerprint recognition with embedded cameras on mobile phones. In: International conference on security and privacy in mobile information and communication systems, pp 136–147. Springer

  6. Ester M, Kriegel HP, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96:226–231

    Google Scholar 

  7. Fei L, Lu G, Jia W, Teng S, Zhang D (2018) Feature extraction methods for palmprint recognition: a survey and evaluation. IEEE Trans Syst Man Cybern Syst 49(2):346–363

    Article  Google Scholar 

  8. Fei L, Xu Y, Teng S, Zhang W, Tang W, Fang X (2017) Local orientation binary pattern with use for palmprint recognition. In: Chinese conference on biometric recognition, pp 213–220. Springer

  9. Fei L, Zhang B, Zhang W, Teng S (2019) Local apparent and latent direction extraction for palmprint recognition. Inf Sci 473:59–72

    Article  Google Scholar 

  10. Feng J, Jain AK (2011) Fingerprint reconstruction: from minutiae to phase. IEEE Trans Pattern Anal Mach Intell 33(2):209–223

    Article  Google Scholar 

  11. Genovese A, Piuri V, Plataniotis KN, Scotti F (2019) PalmNet: Gabor-PCA convolutional networks for touchless palmprint recognition. IEEE Trans Inf Forensic Secur 14:3160–3174

    Article  Google Scholar 

  12. Green-Bit: Green-bit mc517 scanner, http://www.greenbit-china.cn/index.php?m=content&c=index&a=show&catid=36&id=17

  13. Hernandez-Palancar J, Munoz-Briseno A, Gago-Alonso A (2014) Using a triangular matching approach for latent fingerprint and palmprint identification. Int J Pattern Recognit Artif Intell 28(07):1460004

    Article  Google Scholar 

  14. Hiew B, Teoh AB, Ngo DC (2006) Preprocessing of fingerprint images captured with a digital camera. In: 2006 9th international conference on control, automation, robotics and vision, pp 1–6. IEEE

  15. HP-Inc: Hp scanjet g4010 series scanner, https://support.hp.com/us-en/document/c00817232

  16. Jain A, Demirkus M (2008) On latent palmprint matching. Tech. Rep. 48824, Michigan State University

  17. Jain AK, Feng J (2009) Latent palmprint matching. IEEE Trans Pattern Anal Mach Intell 31(6):1032–1047

    Article  Google Scholar 

  18. Khan S, Waqas A, Khan MA, Ahmad AW (2018) A camera-based fingerprint registration and verification method. Int J Comput Sci Netw Secur 18(11):26–31

    Google Scholar 

  19. Kumar A, Wong DC, Shen HC, Jain AK (2003) Personal verification using palmprint and hand geometry biometric. In: International conference on audio-and video-based biometric person authentication, pp 668–678. Springer

  20. Lee C, Lee S, Kim J, Kim SJ (2006) Preprocessing of a fingerprint image captured with a mobile camera. In: International conference on biometrics, pp 348–355. Springer

  21. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2002) Fvc2002: second fingerprint verification competition. In: Object recognition supported by user interaction for service robots, vol 3, pp 811–814. IEEE

  22. Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition. Springer Science & Business Media

  23. Morales A, Ferrer MA, Kumar A (2011) Towards contactless palmprint authentication. IET Comput Vis 5(6):407–416

    Article  MathSciNet  Google Scholar 

  24. Neurotechnology-Inc: Megamatcher (sdk), https://www.neurotechnology.com/cgi-bin/biometric-components.cgi?ref=mm&component=palm-mat

  25. Neurotechnology-Inc (2004) Verifinger 4.2 (sdk), http://www.neurotechnologija.com/download.html

  26. Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations. J Comput Phys 79(1):12–49

    Article  MathSciNet  Google Scholar 

  27. Parihar AS, Kumar A, Verma OP, Gupta A, Mukherjee P, Vatsa D (2013) Point based features for contact-less palmprint images. In: 2013 IEEE International conference on technologies for homeland security (HST), pp 165–170. IEEE

  28. Reza AM (2004) Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. J VLSI Signal Process Syst Signal Image Video Technol 38(1):35–44

    Article  Google Scholar 

  29. Uhl A, Wild P (2008) Personal recognition using single-sensor multimodal hand biometrics. In: International conference on image and signal processing, pp 396–404. Springer

  30. Uhl A, Wild P (2013) Experimental evidence of ageing in hand biometrics. In: 2013 international conference of the BIOSIG Special Interest Group (BIOSIG), pp 1–6. IEEE

  31. Wang R, Ramos D, Fierrez J (2011) Latent-to-full palmprint comparison based on radial triangulation under forensic conditions. In: 2011 International joint conference on biometrics (IJCB), pp 1–6. IEEE

  32. Wang W, Li J, Huang F, Feng H (2008) Design and implementation of log-gabor filter in fingerprint image enhancement. Pattern Recogn Lett 29(3):301–308

    Article  Google Scholar 

  33. Whitaker J (2005) The electronics handbook, 2nd edn. CRC Press

  34. Wu X, Zhao Q, Bu W (2014) A sift-based contactless palmprint verification approach using iterative ransac and local palmprint descriptors. Pattern Recogn 47(10):3314–3326

    Article  Google Scholar 

  35. Zhong D, Du X, Zhong K (2019) Decade progress of palmprint recognition: a brief survey. Neurocomputing 328:16–28

    Article  Google Scholar 

  36. Zhou K, Zhou X, Yu L, Shen L, Yu S (2019) Double biologically inspired transform network for robust palmprint recognition. Neurocomputing 337:24–45

    Article  Google Scholar 

Download references

Acknowledgements

This project was partially funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 690907 (IDENTITY).

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Correspondence to Manuel Aguado-Martínez.

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Aguado-Martínez, M., Hernández-Palancar, J., Castillo-Rosado, K. et al. Document scanners for minutiae-based palmprint recognition: a feasibility study. Pattern Anal Applic 24, 459–472 (2021). https://doi.org/10.1007/s10044-020-00923-3

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