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Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3649–3688 | Cite as

Impact of digital fingerprint image quality on the fingerprint recognition accuracy

  • Mohammad A. AlsmiratEmail author
  • Fatimah Al-Alem
  • Mahmoud Al-Ayyoub
  • Yaser Jararweh
  • Brij Gupta
Article

Abstract

Despite the large body of work on fingerprint identification systems, most of it focused on using specialized devices. Due to the high price of such devices, some researchers directed their attention to digital cameras as an alternative source for fingerprints images. However, such sources introduce new challenges related to image quality. Specifically, most digital cameras compress captured images before storing them leading to potential losses of information. This study comes to address the need to determine the optimum ratio of the fingerprint image compression to ensure the fingerprint identification system’s high accuracy. This study is conducted using a large in-house dataset of raw images. Therefore, all fingerprint information is stored in order to determine the compression ratio accurately. The results proved that the used software functioned perfectly until a compression ratio of (30–40%) of the raw images; any higher ratio would negatively affect the accuracy of the used system.

Keywords

Fingerprint recognition Digital cameras Raw images JPEG compression 

Notes

Acknowledgments

This work is supported by the Jordan University of Science and Technology Deanship of Research project number 20150348.

References

  1. 1.
    Al-alem F, Alsmirat MA, Al-Ayyoub M (2016) On the road to the internet of biometric things: a survey of fingerprint acquisition technologies and fingerprint databases. In: 13th ACS/IEEE international conference on computer systems and applications (AICCSA 2016). IEEEGoogle Scholar
  2. 2.
    Behera B, Lalwani A, Awate A (2014) Using webcam to enhance fingerprint recognition. In: Articulated motion and deformable objects, pp 51–60. SpringerGoogle Scholar
  3. 3.
    Bhargava N, Bhargava DR, Mathuria M, Dixit P (2013) Fingerprint minutiae matching using region of interest. International Journal of Computer Trends and Technology 4(4):515–518Google Scholar
  4. 4.
    Biometric Recognition Group - ATVS. http://atvs.ii.uam.es/. Accessed: 2016-08-16
  5. 5.
    Cappelli R, Ferrara M, Franco A, Maltoni D (2007) Fingerprint verification competition 2006. Biometric Technology Today 15(7):7–9CrossRefGoogle Scholar
  6. 6.
    CASIA-FingerprintV5. http://biometrics.idealtest.org/ (2010). Accessed: 2016-08-16
  7. 7.
    Chang X, Ma Z, Lin M, Yang Y, Hauptmann AG (2017) Feature interaction augmented sparse learning for fast kinect motion detection. IEEE Trans Image Process 26(8):3911–3920.  https://doi.org/10.1109/TIP.2017.2708506 MathSciNetCrossRefGoogle Scholar
  8. 8.
    Daugman J, Downing C (2008) Effect of severe image compression on iris recognition performance. IEEE Transactions on Information Forensics and Security 3 (1):52–61CrossRefGoogle Scholar
  9. 9.
    Derawi MO, Gafurov D, Larsen R, Busch C, Bours P (2010) Fusion of gait and fingerprint for user authentication on mobile devices. In: The 2nd international workshop on security and communication networks (IWSCN), pp 1–6. IEEEGoogle Scholar
  10. 10.
    Drake D (2008) Fingerprint abstraction layer for linuxGoogle Scholar
  11. 11.
    Funk W, Arnold M, Busch C, Munde A (2005) Evaluation of image compression algorithms for fingerprint and face recognition systems. In: Proceedings from the 6th annual IEEE SMC information assurance workshop, pp 72–78. IEEEGoogle Scholar
  12. 12.
    FVC2006: the Fourth International Fingerprint Verification Competition. http://bias.csr.unibo.it/fvc2006/ (2006). Accessed: 2016-08-13
  13. 13.
    Hannah JG, Gladis D (2015) Feature extraction with thinning algorithms for precise cretoscopy. Int. J. Comput. Appl. 8(29):1–7Google Scholar
  14. 14.
    Hannah GJ, GD (2014) Dactyloscopy and comparison of algorithms for efficacious minutiae extraction. In: International conference on advance research in engineering and technology, pp 52–57Google Scholar
  15. 15.
    Hassanat AB, Alkasassbeh M, Al-awadi M, Alhasanat EA (2015) Colour-based lips segmentation method using artificial neural networks. In: 2015 6th international conference on information and communication systems (ICICS), pp 188–193.  https://doi.org/10.1109/IACS.2015.7103225
  16. 16.
    Hiew BY, Teoh ABJ, Yin OS (2010) A secure digital camera based fingerprint verification system. J Vis Commun Image Represent 21(3):219–231.  https://doi.org/10.1016/j.jvcir.2009.12.003. http://www.sciencedirect.com/science/article/pii/S1047320309001576 CrossRefGoogle Scholar
  17. 17.
    Hu C, Yin J, Zhu E, Chen H, Li Y (2010) A composite fingerprint segmentation based on log-gabor filter and orientation reliability. In: 17Th IEEE international conference on image processing, pp 3097–3100. IEEEGoogle Scholar
  18. 18.
    Irtaza A, Jaffar MA (2015) Categorical image retrieval through genetically optimized support vector machines (gosvm) and hybrid texture features. SIViP 9 (7):1503–1519.  https://doi.org/10.1007/s11760-013-0601-8 CrossRefGoogle Scholar
  19. 19.
    Islam MR, Sayeed MS, Samraj A et al (2008) Fingerprint authentication system using a low-priced webcam. In: The international conference on data management (ICDM 2008), IMT Ghaziabad, India, pp 689–697Google Scholar
  20. 20.
    Islam MR, Sayeed MS, Samraj A et al (2010) Technology review: image enhancement, feature extraction and template protection of a fingerprint authentication system. J Appl Sci (Faisalabad) 10(14):1397– 1404CrossRefGoogle Scholar
  21. 21.
    Ives RW, Broussard RP, Kennell LR, Soldan DL (2008) Effects of image compression on iris recognition system performance. Journal of Electronic Imaging 17 (1):011,015–011,015–8.  https://doi.org/10.1117/1.2891313 CrossRefGoogle Scholar
  22. 22.
    Jain AK, Arora SS, Best-Rowden L, Cao K, Sudhish PS, Bhatnagar A (2015) Biometrics for child vaccination and welfare: Persistence of fingerprint recognition for infants and toddlers. arXiv:1504.04651
  23. 23.
    Johnson P, Hua F, Schuckers S (2013) Texture modeling for synthetic fingerprint generation. In: the IEEE conference on computer vision and pattern recognition workshops, pp 154–159.  https://doi.org/10.1109/CVPRW.2013.30
  24. 24.
    Jung SM (2013) Design of low power anf high speed cmos fingerprint sensor. International Journal of Bio-Science and Bio-Technology 5(2)Google Scholar
  25. 25.
    K PV, Pradsad G, Chandrasekhar B (2013) Image compression effects on face recognition for images with reduction in size International Journal of Computer Applications 61(22)Google Scholar
  26. 26.
    Khalil MS (2015) Reference point detection for camera-based fingerprint image based on wavelet transformation. Biomedical engineering online 14(1):40CrossRefGoogle Scholar
  27. 27.
    Kumar A, Jilani TA (2015) A simple and efficient roadmap to process fingerprint images in frequency domain. Int J Comput Appl 112(4):19–25Google Scholar
  28. 28.
    Kurniawan F, Khalil MS, Khan MK (2013) Core-point detection on camera-based fingerprint image. In: International symposium on biometrics and security technologies (ISBAST), pp 241–246. IEEEGoogle Scholar
  29. 29.
    Lee HC, Ramotowski R, Gaensslen RE (2001) Advances in fingerprint technology, 2nd edn. CRC press, Boca RatonCrossRefGoogle Scholar
  30. 30.
    Li G, Yang B, Busch C (2013) Lightweight quality metrics for smartphone camera based fingerprint samples. In: 9th international conference on intelligent information hiding and multimedia signal processing, pp 342–345. IEEEGoogle Scholar
  31. 31.
    Liu E, Zhao H, Guo F, Liang J, Tian J (2011) Fingerprint segmentation based on an adaboost classifier. Frontiers of Computer Science in China 5(2):148–157MathSciNetCrossRefGoogle Scholar
  32. 32.
    Ma L, Tan T, Wang Y, Zhang D (2004) Efficient iris recognition by characterizing key local variations. IEEE Trans Image Process 13(6):739–750CrossRefGoogle Scholar
  33. 33.
    Maio D, Maltoni D, Cappelli R, Wayman J, Jain AK (2002) Fvc2002: Second fingerprint verification competition. In: Proceedings of 16th international conference on pattern recognition (ICPR2002), Quebec City, pp 811–814Google Scholar
  34. 34.
    Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2004) FVC2004: 3rd fingerprint verification competition, pp 1–7. Springer, Berlin.  https://doi.org/10.1007/978-3-540-25948-0_1 Google Scholar
  35. 35.
    Maio D, Maltoni D, Cappelli R, Wayman JL, Jain PK. FVC2000: Fingerprint Verification Competition. Tech. rep. (2000). [Online: http://bias.csr.unibo.it/fvc2000/default.asp, accessed 13-August-2016]
  36. 36.
    Mascher-Kampfer A, Stögner H, Uhl A (2007) Comparison of compression algorithms’ impact on fingerprint and face recognition accuracy. In: Visual communications and image processing, pp 650,810–1Google Scholar
  37. 37.
    Modi SK, Elliott SJ (2006) Impact of image quality on performance: comparison of young and elderly fingerprints. In: Sirlantzis K (ed) Proceedings of the 6th international conference on recent advances in soft computing (RASC 2006), pp 449–45Google Scholar
  38. 38.
    Modi SK, Elliott SJ, Whetsone J, Kim H (2007) Impact of age groups on fingerprint recognition performance. In: 2007 IEEE Workshop on Automatic Identification Advanced Technologies, pp 19–23.  https://doi.org/10.1109/AUTOID.2007.380586
  39. 39.
    Mohammedsayeemuddin S, Pithadia PV, Vandra D (2014) A simple and novel fingerprint image segmentation algorithm. In: International conference on issues and challenges in intelligent computing techniques (ICICT), pp 756–759. IEEEGoogle Scholar
  40. 40.
    Mueller R, Sanchez-Reillo R (2009) An approach to biometric identity management using low cost equipment. In: 5th international conference on intelligent information hiding and multimedia signal processing, pp 1096–1100. IEEEGoogle Scholar
  41. 41.
    NIST Biometric Image Software. http://www.nist.gov/itl/iad/ig/nbis.cfm (2015). Accessed: 2016-08-16
  42. 42.
    Patel V, Thacker K, Shah APV (2014) An approach for fingerprint recognition based on minutia points. International Journal of Advance Engineering and Research Development 1(4):1–9CrossRefGoogle Scholar
  43. 43.
    Piuri V, Scotti F (2008) Fingerprint biometrics via low-cost sensors and webcams. In: 2nd IEEE international conference on biometrics: theory, applications and systems, pp 1–6. IEEEGoogle Scholar
  44. 44.
    Raghavendra R, Busch C, Yang B (2013) Scaling-robust fingerprint verification with smartphone camera in real-life scenarios. In: IEEE 6th international conference on biometrics: theory, applications and systems (BTAS), pp 1–8. IEEEGoogle Scholar
  45. 45.
    Saad MA, Pinson MH, Nicholas DG, Van Kets N, Van Wallendael G, Da Silva R, Jaladi RV, Corriveau PJ (2015) Impact of camera pixel count and monitor resolution perceptual image quality. In: Colour and visual computing symposium (CVCS), 2015, pp 1–6. IEEEGoogle Scholar
  46. 46.
    Sahu D, Shrivas R (2013) Fingerprint reorganization using minutiae based matching for identification and verification. International Journal of Science and ResearchGoogle Scholar
  47. 47.
    Sankaran A, Dhamecha TI, Vatsa M, Singh R (2011) On matching latent to latent fingerprints. In: 2011 international joint conference on biometrics (IJCB), pp 1–6, DOI  https://doi.org/10.1109/IJCB.2011.6117525, (to appear in print)
  48. 48.
    Sankaran A, Vatsa M, Singh R (2012) Hierarchical fusion for matching simultaneous latent fingerprint. In: IEEE 5th international conference on biometrics: theory, applications and systems (BTAS).  https://doi.org/10.1109/BTAS.2012.6374604, pp 377–382
  49. 49.
    Sankaran A, Vatsa M, Singh R (2015) Multisensor optical and latent fingerprint database. IEEE Access 3:653–665.  https://doi.org/10.1109/ACCESS.2015.2428631 CrossRefGoogle Scholar
  50. 50.
    Setlak D (1999) Electric field fingerprint sensor apparatus and related methods. https://www.google.com/patents/US5963679. US Patent 5,963,679
  51. 51.
    Shobhraj NR, Kidwai MA (2014) Fingerprint recognition system. International Journal of Innovative Science, Engineering and Technology 1(3):2348–7968Google Scholar
  52. 52.
    Silvestre-Blanes J (2015) Scalability in industrial image processing applications. In: Telecommunications forum telfor (TELFor), 2015 23rd, pp 744–747. IEEEGoogle Scholar
  53. 53.
    Stoney DA (1988) Distribution of epidermal ridge minutiae. Am J Phys Anthropol 77(3):367–376.  https://doi.org/10.1002/ajpa.1330770309 CrossRefGoogle Scholar
  54. 54.
    Thai R (2003) Fingerprint image enhancement and minutiae extraction. Ph.D. thesis, Computer Science and software engineering University of western AustraliaGoogle Scholar
  55. 55.
    Teoh AB, Ngo DC (2006) Preprocessing of fingerprint images captured with a digital camera. In: The 9th international conference on control, automation, robotics and vision (ICARCV), pp 1–6. IEEEGoogle Scholar
  56. 56.
    Tong XF, Li PF (2011) Fingerprint image segmentation based on fingerprint ridge intensity. In: International conference on machine learning and cybernetics (ICMLC), vol 4, pp 1780–1784. IEEEGoogle Scholar
  57. 57.
    Uysal M, Gorgunoglu S (2014) Ridge pattern representation for fingerprint indexing. Elektronika ir Elektrotechnika 20(7):65–68CrossRefGoogle Scholar
  58. 58.
    Webb L, Mathekga M (2014) Towards a complete rule-based classification approach for flat fingerprints. In: 2nd international symposium on computing and networking, pp 549–555. IEEEGoogle Scholar
  59. 59.
    Wu J, Bisio I, Gniady C, Hossain E, Valla M, Li H (2014) Context-aware networking and communications: Part 1 [guest editorial]. IEEE Commun Mag 52(6):14–15.  https://doi.org/10.1109/MCOM.2014.6829939 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science DepartmentJordan University of Science and TechnologyIrbidJordan
  2. 2.National Institute of Technology KurukshetraKurukshetraIndia

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