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The Resolution for Fingerprint Recognition

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Advanced Biometrics

Abstract

High-resolution automated fingerprint recognition systems (AFRS) offer higher security because they are able to make use of level 3 features, such as pores, that are not available in lower-resolution (<500 dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and performance of an AFRS is the choice of image resolution. In this chapter, we identify the optimal resolution for an AFRS using the two most representative fingerprint features, minutiae and pores. We first designed a multi-resolution fingerprint acquisition device to collect fingerprint images at multiple resolutions and captured fingerprints at various resolutions but at a fixed image size. We then carried out a theoretical analysis to identify the minimum required resolution for fingerprint recognition using minutiae and pores. After experiments on our collected fingerprint images and applying three requirements for the proportions of minutiae and pores that must be retained in a fingerprint image, we recommend a reference resolution of 800 dpi. Subsequent tests have further confirmed the proposed reference resolution.

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References

  • Acree M (1999) Is there an gender difference in fingerprint ridge density? Forensic Sci Int 102(1):35–44

    Article  Google Scholar 

  • Ashbaugh D (1999) Quantitative–qualitative friction ridge analysis: an introduction to basic and advanced ridgeology. CRC Press, Boca Raton, FL

    Book  Google Scholar 

  • Bicz W, Banasiak D, Bruciak P, Gumienny S, Gumulinski D, Keysiak A, Kuczynski W, Pluta M, Rabiej G. Fingerprint structure imaging based on an ultrasound camera [online]. http://www.optel.com.pl/article/english/article.htm

  • CDEFFS (2009) Data format for the interchange of extended fingerprint and palmprint features, version 0.4

    Google Scholar 

  • Chen Y (2009) Extended feature set and touchless imaging for fingerprint matching. Michigan State University

    Google Scholar 

  • Cummins H, Waits W, McQuitty J (1941) The breadths of epidermal ridges on the finger tips and palms: a study of variation. Am J Anat 68(1):127–150

    Article  Google Scholar 

  • Han J, Tan Z, Sato K, Shikida M (2005) Thermal characterization of micro heater arrays on a polyimide film substrate for fingerprint sensing applications. J Micromech Microeng 15(2):282–289

    Article  Google Scholar 

  • International Biometric Group (2008) Analysis of level 3 features at high resolutions (phase II)

    Google Scholar 

  • Jain A, Hong L, Bolle R (1997) On-line fingerprint verification. IEEE Trans Pattern Anal Mach Intell 19(4):302–314

    Article  Google Scholar 

  • Jain A, Chen Y, Demirkus M (2006) Pores and ridges: fingerprint matching using level 3 features. In: Proceedings of the 18th international conference on pattern recognition, pp 477–480

    Google Scholar 

  • Jain A, Chen Y, Demirkus M (2007) Pores and ridges: high-resolution fingerprint matching using level-3 features. IEEE Trans Pattern Anal Mach Intell 29(1):15–27

    Article  Google Scholar 

  • Kryszczuk K, Drygajlo A, Morier P (2004a) Extraction of level 2 and level 3 features for fragmentary fingerprints. In: Proceedings of the 2nd COST Action 275 workshop, Vigo, Spain, pp 83–88

    Google Scholar 

  • Kryszczuk K, Morier P, Drygajlo A (2004b) Study of the distinctiveness of the level 2 and level 3 features in fragmentary fingerprint comparison. In: Proceedings of the biometrics authentication, ECCV 2004 international workshop, pp 124–133

    Google Scholar 

  • Maltoni D, Maio D, Jain A, Prabhakar S (2009) Handbook of fingerprint recognition. Springer, New York

    Book  MATH  Google Scholar 

  • Parsons N, Smith J, Thonnes E, Wang L, Wilson R (2008) Rotationally invariant statistics for examining the evidence from the pores in fingerprints. Law Probab Risk 7(1):1–14

    Article  Google Scholar 

  • Ratha N, Bolle R (2004) Automatic fingerprint recognition systems. Springer, New York

    Book  Google Scholar 

  • Ray M, Meenen P, Adhami R (2005) A novel approach to fingerprint pore extraction. In: Proceedings of the thirty-seventh southeastern symposium on system theory, pp 282–286

    Google Scholar 

  • Roddy A, Stosz J (1997) Fingerprint features-statistical analysis and system performance estimates. Proc IEEE 85(9):1390–1421

    Article  Google Scholar 

  • Ross A, Jain A (2004) Biometric sensor interoperability: a case study in fingerprints. In: Proceedings of the international ECCV workshop on biometric authentication, LNCS 3087, pp 134–145

    Google Scholar 

  • Stosz J, Alyea L (1994) Automated system for fingerprint authentication using pores and ridge structure. Proc SPIE 2277:210–223

    Google Scholar 

  • Xia X, O’Gorman L (2003) Innovation in fingerprint capture devices. Pattern Recogn 36(2):361–369

    Article  Google Scholar 

  • Zhao Q, Zhang L, Zhang D, Luo N, Bao J (2008) Adaptive pore model for fingerprint pore extraction. In: Proceedings of the 18th international conference on pattern recognition, pp 1–4

    Google Scholar 

  • Zhao Q, Zhang L, Zhang D, Luo N (2009a) Direct pore matching for fingerprint recognition. In: ICB 2009, pp 597–606

    Google Scholar 

  • Zhao Q, Zhang D, Zhang L, Luo N (2009b) High resolution partial fingerprint alignment using pore-valley descriptors. Pattern Recogn 43(3):1050–1061

    Article  MATH  Google Scholar 

Download references

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Zhang, D., Lu, G., Zhang, L. (2018). The Resolution for Fingerprint Recognition. In: Advanced Biometrics. Springer, Cham. https://doi.org/10.1007/978-3-319-61545-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-61545-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61544-8

  • Online ISBN: 978-3-319-61545-5

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